CANCER MORTALITY DATA ANALYSIS AND PREDICTION

Tradizionalmente, l\u2019epidemiologia descrittiva viene considerata come un semplice strumento esplorativo. Tuttavia, nel corso degli anni, la maggiore disponibilit\ue0 e il miglioramento della qualit\ue0 dei dati epidemiologici hanno portato allo sviluppo di nuove tecniche statistiche che caratterizzano l'epidemiologia moderna. Questi metodi non sono solo esplicativi, ma anche predittivi. In ambito di sanit\ue0 pubblica, le previsioni degli andamenti futuri di morbilit\ue0 e mortalit\ue0 sono essenziali per valutare le strategie di prevenzione, la gestione delle malattie e per pianificare l'allocazione delle risorse. Durante il mio dottorato di ricerca in "Epidemiologia, Ambiente e Sanit\ue0 Pubblica" ho lavorato all'analisi degli andamenti di mortalit\ue0 per tumore, utilizzando principalmente la banca dati della World Health Organization (WHO), ma anche quella della Pan American Health Organization, dell\u2019Eurostat, della United Nation Population Division, dello United States Census Bureau e la banca dati del Japanese National Institute of Population. Considerando diversi siti neoplastici e diversi paesi nel mondo, ho calcolato i tassi specifici per ogni classe di et\ue0 quinquennale (da 0-4 a 80+ o 85+ anni), e singolo anno di calendario o quinquennio. Per poter confrontare i tassi fra diversi paesi, ho calcolato, utilizzando il metodo diretto sulla base della popolazione mondiale standard, i tassi di mortalit\ue0 standardizzati per et\ue0 per 100.000 anni-persona. Nella maggior parte delle analisi, ho poi applicato il modello di regressione joinpoint ai tassi standardizzati con lo scopo di individuare gli anni in cui erano avvenuti cambiamenti significativi nell\u2019andamento dei tassi; per ogni segmento individuato dalla regressione joinpoint, ho calcolato le variazioni percentuali annue. Inoltre, mi sono concentrata sulle proiezioni degli andamenti futuri. Con l\u2019obiettivo di individuare il segmento pi\uf9 recente dell\u2019andamento di mortalit\ue0, ho applicato il modello di regressione joinpoint al numero di morti in ogni gruppo di et\ue0 quinquennale. Quindi, ho utilizzato i Modelli Lineari Generalizzati (GLM), scegliendo la distribuzione di Poisson e diverse funzioni link, sui dati dell\u2019ultimo segmento individuato dal modello joinpoint. In particolare, ho considerato le funzioni link identit\ue0, logaritmica, quinta potenza e radice quadrata. Ho anche implementato un algoritmo che genera una regressione "ibrida"; questo algoritmo seleziona automaticamente, in base al valore della statistica Akaike Information Criterion (AIC), il modello GLM Poisson pi\uf9 performante, tra quelli generati dalle funzioni link di identit\ue0, logaritmica, quinta potenza e radice quadrata, da applicare a ciascuna classe di et\ue0 quinquennale. La regressione risultante, sull\u2019insieme dei singoli gruppi di et\ue0, \ue8 quindi una combinazione dei modelli considerati. Quindi, applicando i coefficienti ottenuti dalle quattro regressioni GLM Poisson e dalla regressione ibrida sugli anni di previsione, ho ottenuto le stime predette del numero di morti. A seguire, utilizzando il numero di morti predetto e le popolazioni predette, ho stimato i tassi previsti specifici per et\ue0 e i corrispondenti intervalli di previsione al 95% (PI). Infine, come ulteriore modello di confronto, ho costruito un modello medio, che semplicemente calcola una media delle stime prodotte dai diversi modelli GLM Poisson. Al fine di confrontare fra loro i sei diversi metodi di previsione, ho utilizzato i dati relativi a 21 paesi in tutto il mondo e all'Unione Europea nel suo complesso, e ho considerato 25 maggiori cause di morte. Ho selezionato solo i paesi con oltre 5 milioni di abitanti e solo i paesi per i quali erano disponibili dati di buona qualit\ue0 (ovvero con almeno il 90% di coverage). Ho analizzato i dati del periodo temporale compreso tra il 1980 e il 2011 e, in particolare, ho applicato i vari modelli sui dati dal 1980 al 2001 con l\u2019idea di prevedere i tassi sul periodo 2002-2011, e ho poi utilizzato i dati effettivamente disponibili dal 2002 al 2011 per valutare le stime predette. Quindi, per misurare l'accuratezza predittiva dei diversi metodi, ho calcolato la deviazione relativa assoluta media (AARD). Questa quantit\ue0 indica la deviazione media percentuale del valore stimato dal valore vero. Ho calcolato gli AARD su un periodo di previsione di 5 anni (i.e. 2002-2006), e anche su un periodo di 10 anni (i.e. 2002-2011). Dalle analisi \ue8 emerso che il modello ibrido non sempre forniva le migliori stime di previsione e, anche quando risultava il migliore, i corrispondenti valori di AARD non erano poi molto lontani da quelli degli altri metodi. Tuttavia, le proiezioni ottenute utilizzando il modello ibrido, per qualsiasi combinazione di sito di tumore e sesso, non sono mai risultate le peggiori. Questo modello \ue8 una sorta di compromesso tra le quattro funzioni link considerate. Anche il modello medio fornisce stime intermedie rispetto alle altre regressioni: non \ue8 mai risultato il miglior metodo di previsione, ma i suoi AARD erano competitivi rispetto agli altri metodi considerati. Complessivamente, il modello che mostra le migliori prestazioni predittive \ue8 il GLM Poisson con funzione link identit\ue0. Inoltre, questo metodo ha mostrato AARD estremamente bassi rispetto agli altri metodi, in particolare considerando un periodo di proiezione di 10 anni. Infine, bisogna tenere in considerazione che gli andamenti previsti, e i corrispondenti AARD, ottenuti da proiezioni su periodi di 5 anni sono molto pi\uf9 accurati rispetto a quelli su periodi di 10 anni. Le proiezioni ottenute con questi metodi per periodi superiori a 5 anni perdono in affidabilit\ue0 e la loro utilit\ue0 in sanit\ue0 pubblica risulta quindi limitata. Durante l'implementazione della regressione ibrida e durante le analisi sono rimaste aperte alcune questioni: ci sono altri modelli rilevanti che possono essere aggiunti all'algoritmo? In che misura la regressione joinpoint influenza le proiezioni? Come trovare una regola "a priori" che aiuti a scegliere quale metodo predittivo applicare in base alle varie covariate disponibili? Tutte queste domande saranno tenute in considerazione per gli sviluppi futuri del progetto. Prevedere gli andamenti futuri \ue8 un processo complesso, le stime risultanti dovrebbero quindi essere considerate con cautela e solo come indicazioni generali in ambito epidemiologico e di pianificazione sanitaria.Descriptive epidemiology has traditionally only been concerned with the definition of a research problem\u2019s scope. However, the greater availability and improvement of epidemiological data over the years has led to the development of new statistical techniques that have characterized modern epidemiology. These methods are not only explanatory, but also predictive. In public health, predictions of future morbidity and mortality trends are essential to evaluate strategies for disease prevention and management, and to plan the allocation of resources. During my PhD at the school of \u201cEpidemiology, Environment and Public Health\u201d I worked on the analysis of cancer mortality trends, using data from the World Health Organization (WHO) database, available on electronic support (WHOSIS), and from other databases, including the Pan American Health Organization database, the Eurostat database, the United Nation Population Division database, the United States Census Bureau and the Japanese National Institute of Population database. Considering several cancer sites and several countries worldwide, I computed age-specific rates for each 5-year age-group (from 0\u20134 to 80+ or 85+ years) and calendar year or quinquennium. I then computed age-standardized mortality rates per 100,000 person-years using the direct method on the basis of the world standard population. I performed joinpoint models in order to identify the years when significant changes in trends occurred and I calculated the corresponding annual percent changes. Moreover, I focused on projections. I fitted joinpoint models to the numbers of certified deaths in each 5-year age-group in order to identify the most recent trend slope. Then, I applied Generalized Liner Model (GLM) Poisson regressions, considering different link functions, to the data over the time period identified by the joinpoint model. In particular, I considered the identity link, the logarithmic link, the power five link and the square root link. I also implemented an algorithm that generated a \u201chybrid\u201d regression; this algorithm automatically selects the best fitting GLM Poisson model, among the identity, logarithmic, power five, and square root link functions, to apply for each age-group according to Akaike Information Criterion (AIC) values. The resulting regression is a combination of the considered models. Thus, I computed the predicted age-specific numbers of deaths and rates, and the corresponding 95% prediction intervals (PIs) using the regression coefficients obtained previously from the four GLM Poisson regressions and from the hybrid GLM Poisson regression. Lastly, as a further comparison model, I implemented an average model, which just computes a mean of the estimates produced by the different considered GLM Poisson models. In order to compare the six different prediction methods, I used data from 21 countries worldwide and for the European Union as a whole, I considered 25 major causes of death. I selected countries with over 5 million inhabitants and with good quality data (i.e. with at least 90% of coverage). I analysed data for the period between 1980 and 2011 and, in particular, I considered data from 1980 to 2001 as a training dataset, and from 2002 to 2011 as a validation set. To measure the predictive accuracy of the different models, I computed the average absolute relative deviations (AARDs). These indicate the average percent deviation from the true value. I calculated AARDs on 5-year prediction period (i.e. 2002-2006), as well as for 10-year period (i.e. 2002-2011). The results showed that the hybrid model did not give always the best predictions, and when it was the best, the corresponding AARD estimates were not very far from the other methods. However, the hybrid model projections, for any combination of cancer site and sex, were never the worst. It acted as a compromise between the four considered models. The average model is also ranked in an intermediate position: it never was the best predictive method, but its AARDs were competitive compared to the other methods considered. Overall, the method that shows the best predictive performance is the Poisson GLM with an identity link function. Furthermore, this method, showed extremely low AARDs compared to other methods, particularly when I considered a 10-year projection period. Finally, we must take into account that predicted trends and corresponding AARDs derived from 5-year projections are much more accurate than those done over a 10-year period. Projections beyond five years with these methods lack reliability and become of limited use in public health. During the implementation of the algorithm and the analyses, several questions emerged: Are there other relevant models that can be added to the algorithm? How much does the Joinpoint regression influence projections? How to find an \u201ca priori\u201d rule that helps in choosing which predictive method apply according to various available covariates? All these questions are set aside for the future developments of the project. Prediction of future trends is a complex procedure, the resulting estimates should be taken with caution and considered only as general indications for epidemiology and health planning

European cancer mortality predictions for the year 2019 with focus on breast cancer

Background We predicted cancer mortality figures in the European Union (EU) for the year 2017 using most recent available data, with a focus on lung cancer. Materials and methods We retrieved cancer death certification data and population figures from the World Health Organisation and Eurostat databases. Age-standardized (world standard population) rates were computed for France, Germany, Italy, Poland, Spain, the UK and the EU overall in 1970–2012. We obtained estimates for 2017 by implementing a joinpoint regression model. Results The predicted number of cancer deaths for 2017 in the EU is 1 373 500, compared with 1 333 400 in 2012 (+3%). Cancer mortality rates are predicted to decline in both sexes, reaching 131.8/100 000 men (−8.2% when compared with 2012) and 84.5/100 000 women (−3.6%). Mortality rates for all selected cancer sites are predicted to decline, except pancreatic cancer in both sexes and lung cancer in women. In men, pancreatic cancer rate is stable, in women it increases by 3.5%. Lung cancer mortality rate in women is predicted to rise to 14.6/100 000 in 2017 (+5.1% since 2012, corresponding to 92 300 predicted deaths), compared with 14.0/100 000 for breast cancer, corresponding to 92 600 predicted deaths. Only younger (25–44) women have favourable lung cancer trends, and rates at this age group are predicted to be similar in women (1.4/100 000) and men (1.2/100 000). In men lung cancer rates are predicted to decline by 10.7% since 2012, and falls are observed in all age groups. Conclusion European cancer mortality projections for 2017 confirm the overall downward trend in rates, with a stronger pattern in men. This is mainly due to different smoking prevalence trends in different generations of men and women. Lung cancer rates in young European women are comparable to those in men, confirming that smoking has the same impact on lung cancer in the two sexes

European cancer mortality predictions for the year 2020 with a focus on prostate cancer

Background: Current cancer mortality figures are important for disease management and resource allocation. We estimated mortality counts and rates for 2020 in the European Union (EU) and for its six most populous countries. Materials and methods: We obtained cancer death certification and population data from the World Health Organization and Eurostat databases for 1970-2015. We estimated projections to 2020 for 10 major cancer sites plus all neoplasms and calculated the number of avoided deaths over 1989-2020. Results: Total cancer mortality rates in the EU are predicted to decline reaching 130.1/100 000 men (-5.4% since 2015) and 82.2 in women (-4.1%) in 2020. The predicted number of deaths will increase by 4.7% reaching 1 428 800 in 2020. In women, the upward lung cancer trend is predicted to continue with a rate in 2020 of 15.1/100 000 (higher than that for breast cancer, 13.5) while in men we predicted further falls. Pancreatic cancer rates are also increasing in women (+1.2%) but decreasing in men (-1.9%). In the EU, the prostate cancer predicted rate is 10.0/100 000, declining by 7.1% since 2015; decreases for this neoplasm are 3c8% at age 45-64, 14% at 65-74 and 75-84, and 6% at 85 and over. Poland is the only country with an increasing prostate cancer trend (+18%). Mortality rates for other cancers are predicted to decline further. Over 1989-2020, we estimated over 5 million avoided total cancer deaths and over 400 000 for prostate cancer. Conclusion: Cancer mortality predictions for 2020 in the EU are favourable with a greater decline in men. The number of deaths continue to rise due to population ageing. Due to the persistent amount of predicted lung (and other tobacco-related) cancer deaths, tobacco control remains a public health priority, especially for women. Favourable trends for prostate cancer are largely attributable to continuing therapeutic improvements along with early diagnosis

The first 110,593 COVID-19 patients hospitalised in Lombardy: a regionwide analysis of case characteristics, risk factors and clinical outcomes

Objectives: To describe the monthly distribution of COVID-19 hospitalisations, deaths and case-fatality rates (CFR) in Lombardy (Italy) throughout 2020. Methods: We analysed de-identified hospitalisation data comprising all COVID-19-related admissions from 1 February 2020 to 31 December 2020. The overall survival (OS) from time of first hospitalisation was estimated using the Kaplan-Meier method. We estimated monthly CFRs and performed Cox regression models to measure the effects of potential predictors on OS. Results: Hospitalisation and death peaks occurred in March and November 2020. Patients aged ≥70 years had an up to 180 times higher risk of dying compared to younger patients [70–80: HR 58.10 (39.14–86.22); 80–90: 106.68 (71.01–160.27); ≥90: 180.96 (118.80–275.64)]. Risk of death was higher in patients with one or more comorbidities [1: HR 1.27 (95% CI 1.20–1.35); 2: 1.44 (1.33–1.55); ≥3: 1.73 (1.58–1.90)] and in those with specific conditions (hypertension, diabetes). Conclusion: Our data sheds light on the Italian pandemic scenario, uncovering mechanisms and gaps at regional health system level and, on a larger scale, adding to the body of knowledge needed to inform effective health service planning, delivery, and preparedness in times of crisis

reply to the letter to the editor european cancer mortality predictions for the year 2019 with focus on breast cancer by malvezzi m et al by marsden and hamoda on behalf of the british menopause society medical advisory council

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Cancer mortality predictions for 2017 in Latin America.

From most recent available data, we predicted cancer mortality statistics in selected Latin American countries for the year 2017, with focus on lung cancer. We obtained death certification data from the World Health Organization and population data from the Pan American Health Organization database for all neoplasms and selected cancer sites. We derived figures for Argentina, Brazil, Chile, Colombia, Cuba, Mexico and Venezuela. Using a logarithmic Poisson count data joinpoint model, we estimated number of deaths and age-standardized (world population) mortality rates in 2017. Total cancer mortality rates are predicted to decline in all countries. The highest mortality rates for 2017 are in Cuba, i.e. 132.3/100 000 men and 93.3/100 000 women. Mexico had the lowest predicted rates, 64.7/100 000 men and 60.6/100 000 women. In contrast, the total number of cancer deaths is expected to rise due to population ageing and growth. Men showed declines in lung cancer trends in all countries and age groups considered, while only Colombian and Mexican women had downward trends. Stomach and (cervix) uteri rates are predicted to continue their declines, though mortality from these neoplasms remains comparatively high. Colorectal, breast and prostate cancer rates were predicted to decline moderately, as well as leukaemias. There was no clear pattern for pancreatic cancer. Between 1990 and 2017 about 420 000 cancer deaths were avoided in 5 of the 7 countries, no progress was observed in Brazil and Cuba. Cancer mortality rates for 2017 in seven selected Latin American countries are predicted to decline, though there was appreciable variability across countries. Mortality from major cancers-including lung and prostate-and all cancers remains comparatively high in Cuba, indicating the need for improved prevention and management

Cancer mortality in the elderly in 11 countries worldwide, 1970-2015

BACKGROUND: Population ageing results in an increasing cancer burden in the elderly. We aimed to evaluate time-trends in cancermortality for adults aged 65 and over for 17 major cancer-types and all cancer combined in 11 countries worldwide over the period 1970-2015. MATERIALS AND METHODS: We obtained cancer death certification and population figures from the WHO and PAHO databases. We computed age-standardized (world standard population) rates for individuals aged 65 and over, and applied joinpoint regression models. RESULTS: Age-standardized mortality rates for all cancers combined showed a heterogeneous, but widespread decline. Lung cancermortality rates have been decreasing among men, and increasing among women. Pancreatic cancer had unfavourable trends in all countriesfor both sexes. Despite variability across countries, other tobacco-related cancers (except kidney) showed overall favourable trends, except in Poland and Russia. Age-standardized mortality from stomach cancer has been declining in all countries for both sexes. Colorectal mortalityhas been declining, except in Poland and Russia. Liver cancer mortality increased in all countries, except in Japan, France and Italy, which had the highest rates in the past. Breast cancer mortality decreased for most countries, except for Japan, Poland and Russia. Trends for age-standardized uterine cancer rates in the USA, Canada and the UK were increasing over the last decade. Ovarian cancer rates showed declines in most countries. With the exception of Russia, prostate cancer rates showed overall declines. Lymphoid neoplasm rates have been declining in both sexes, except in Poland and Russia. CONCLUSION: Over the last decades, age-standardized cancer mortality in the elderly has been decreasing in major countries worldwideand for major cancer sites, with the major exception of lung and uterine cancer in women and liver, pancreas and kidney cancers in both sexes. Cancer mortality for the elderly in Central and Eastern Europe remains comparatively high

Global trends in nasopharyngeal cancer mortality since 1970 and predictions for 2020 : Focus on low-risk areas

Nasopharyngeal cancer (NPC) mortality shows great disparity between endemic high risk areas, where non-keratinizing carcinoma (NKC) histology is prevalent, and non-endemic low risk regions, where the keratinizing squamous cell carcinoma (KSCC) type is more frequent. We used the World Health Organization database to calculate NPC mortality trends from 1970 to 2014 in several countries worldwide. For the European Union (EU), the United States (US) and Japan, we also predicted trends to 2020. In 2012, the highest age-standardized (world standard) rates were in Hong Kong (4.51/100,000 men and 1.15/100,000 women), followed by selected Eastern European countries. The lowest rates were in Northern Europe and Latin America. EU rates were 0.27/100,000 men and 0.09/100,000 women, US rates were 0.20/100,000 men and 0.08/100,000 women and Japanese rates were 0.16/100,000 men and 0.04/100,000 women. NPC mortality trends were favourable for several countries. The decline was 1215% in men and 125% in women between 2002 and 2012 in the EU, 1212% in men and 129% in women in the US and about 1230% in both sexes in Hong Kong and Japan. The favourable patterns in Europe and the United States are predicted to continue. Changes in salted fish and preserved food consumption account for the fall in NKC. Smoking and alcohol prevalence disparities between sexes and geographic areas may explain the different rates and trends observed for KSCC and partially for NKC. Dietary patterns, as well as improvement in management of the disease, may partly account for the observed trends, too

Risk factors for pancreas and lung neuroendocrine neoplasms: a case-control study.

Neuroendocrine neoplasia (NEN) has been displaying an incremental trend along the last two decades. This phenomenon is poorly understood, and little information is available on risk factor for neuroendocrine neoplasia development. Aim of this work is to elucidate the role of potentially modifiable risk factors for pancreatic and pulmonary NEN. We conducted a case-control study on 184 patients with NEN (100 pancreas and 84 lung) and 248 controls. The structured questionnaire included 84 queries on socio-demographic, behavioral, dietary and clinical information. Increased risk was associated with history of cancer ("other tumor", lung OR = 7.18; 95% CI: 2.55-20.20 and pancreas OR = 5.88; 95% CI: 2.43-14.22; "family history of tumor", lung OR = 2.66; 95% CI: 1.53-4.64 and pancreas OR = 1.94; 95% CI: 1.19-3.17; "family history of lung tumor", lung OR = 2.56; 95% CI: 1.05-6.24 and pancreas OR = 2.60; 95% CI: 1.13-5.95). Type 2 diabetes mellitus associated with an increased risk of pancreatic NEN (OR = 3.01; 95% CI: 1.15-7.89). Besides site-specific risk factors, there is a significant link between neuroendocrine neoplasia and cancer in general, pointing to a shared cancer predisposition