Browsing by discipline "Tilastotiede"

The decreasing number of births has caused concerns among researchers and decision-makers and is currently a hot topic in Finland. The most commonly used fertility index, the total fertility rate (TFR), has been rapidly decreasing during the last seven years and reached an all-time low rate of 1.49 children per woman in 2017. The total fertility rate is a synthetic measure that is sensitive to changes in the timing of births and it does not necessarily reflect underlying changes in the level of fertility. A reduction in the total fertility rate could reflect that women are postponing their childbearing while the final number of children they ultimately will have remains unchanged, or, it could reflect that women actually are having less children. The aim with this thesis is to conclude to what extent the decrease in the total fertility rate is due to fertility timing and whether the expressed concern is truly valid. This thesis is a descriptive study produced in collaboration with Statistics Finland. Age-specific fertility rates were calculated by birth order, region and level of education based on data maintained by Statistics Finland. The produced contributions to the decrease in the total fertility rate were analysed by demographic decomposition, tempo-adjusted fertility rates were calculated to adjust for fertility timing and the completed cohort fertility rate for cohorts not yet reached age 44 was estimated mainly by a new Bayesian forecasting method. In addition, high quality fertility data from the Human Fertility Database was used to build a prior belief of already known demographic information about plausible age patterns of fertility. The results confirmed that the main reason for the rapid decrease in the total fertility rate in 2010-2017 was decreasing first order births mainly at ages 25-29. The massive decrease in first order births was observed in both urban and rural areas and by all levels of education, but particularly for higher educated women. Overall, fertility rates at younger ages have experienced a long-term decline while fertility rates at older ages have been increasing. Nevertheless, the fertility rates at ages 30-37 have in recent years also started to decrease. The tempo-adjusted TFR did show a period tempo effect of on average 0.17 live births per woman, but since the adjusted TFR also did decrease since 2010, the possibility that women only postpone but not reduce their number of births is not enough as the only explanation to the all-time low period fertility observed. The cohort fertility forecasts did in fact confirm that women actually are reducing their lifetime number of children. Women currently in their childbearing age have delayed or even eschewed entry to motherhood to such an extent that their average lifetime number of children is very unlikely to remain close to 2 children, which has been the approximately constant level observed over the last thirty years. The completed cohort fertility rate is instead likely to decline dramatically and fall below 1.50 children for women currently in their late 20s. Thus, the decrease in the total fertility rate in 2010-2017 does reflect a massive cohort quantum effect and the expressed concern about the decreasing number of births is indeed very much valid.

In this study, a classification scheme is implemented to obtain high resolution snow cover information from Sentinel-2 data using a very simple Bayesian Network (Naive-Bayes) that is trained with ground snow measurement data. Performance comparison of using Bayesian/non-Bayesian Naive-Bayes, different feature sets and different discretization methods is conducted. Results show that Bayesian NB performs the best with up to 0.88 classification accuracy for snow/no-snow classification. Use of most relevant spectral bands rather than all available bands provided improvement in some cases but also performed slighty worse in some, hence not giving a clear answer. However, effect of discretization method was clear, chimerge performed better than equal width binning but it was much slower to a point that it was not practical to discretisize a full Sentinel-2 image’s pixels.

Modern day technology and computational power have allowed a large scale investigation of the human epigenome. Out of the epigenetic modifications, DNA methylation is of particular interest, since it is relatively easy to measure and very common in the DNA. A methylation site is a region of the DNA sequence that shows variation in the DNA methylation between individuals. Epigenome-wide association studies (EWAS) examine the interaction between these methylation sites one at a time and a specific human trait or an enviromental exposure. EWAS studies are, however, limited by low statistical power and problems related to multiple testing. To counter these issues, polygenic methylation scores have been developed to aggregate information over many methylation sites. These scores have two main applications. First is to formulate new hypotheses to explain human trait variation. Second one is to indicate unobserved environmental factors in cohort based studies or to predict individual developmental or disorder related outcomes. At the beginning of this thesis there is an introduction to epigenetics, to EWAS and polygenic methylation scores and to their genetic counterparts, genome-wide association studies (GWAS) and polygenic risk scores (PRS). Much of the methodology relating to the methylation scores is borrowed from GWAS and PRS. Some statistical properties of the methylation scores are derived in this thesis with focus on how the statistical power of detecting true association between a phenotype and human DNA methylation depends on the make up of the methylation scores. The theoretical derivations are tested through simulations. This thesis also examines how methylation scores may be calculated in practice using cross-validation and correlation reduction procedure called clumping. The methodology is applied to a Finnish cohort from the prediction and prevention of preeclampsia and intrauterine growth restriction study (Predo). The comparison of theoretical and observed statistical power in the simulations show that the theoretical and observed power correspond well to each other. In the practical analyses conducted using the DNA methylation data set and phenotype data of the Predo cohort and a maternal body-mass index (BMI) EWAS data, a clear piece of evidence of association of maternal pre-pregnancy BMI and offspring DNA methylation is found. The results support the growing evidence for the applicability of methylation scores in indicating prenatal environmental factors from the DNA methylation of the offspring.

Thanks to modern medical advances, humans have developed tools for detecting diseases so early, that a patient would be better off had the disease gone undetected. This is called overdiagnosis. Overdiagnosisisaproblemespeciallycommoninacts,wherethetargetpopulationofanintervention consists of mostly healthy people. Colorectal cancer (CRC) is a relatively rare disease. Thus screening for CRC affects mostly cancerfree population. In this thesis I evaluate overdiagnosis in guaiac faecal occult blood test (gFOBT) based CRC screening programme. In gFOBT CRC screening there are two goals: to detect known predecessors of cancers called adenomas and to remove them (cancer prevention), and to detect malign CRCs early enough to be still treatable (early detection). Overdiagnosis can happen when detecting adenomas, but also when detecting cancers. This thesis focuses on overdiagnosis due to detection of adenomas that are non-progressive in their nature. Since there is no clinical means to make distinction between progressive and non-progressive adenomas, statistical methods must be applied. Classical methods to estimate overdiagnosis fail in quantifying this type of overdiagnosis for couple of reasons: incidence data of adenomas is not available, and adenoma removal results in lowering cancer incidence in screened population. While the latter is a desired effect of screening, it makes it impossible to estimate overdiagnosis by just comparing cancer incidences among screened and control populations. In this thesis a Bayesian Hidden Markov model using HMC NUTS algorithm via software Stan is fitted to simulate the natural progression of colorectal cancer. The five states included in the model were healthy (1), progressive adenoma (2), screen-detectable CRC (3), clinically apparent CRC (4) and non-progressive adenoma (5). Possible transitions are from 1 to 2, 1 to 5, 2 to 3 and 3 to 4. The possible observations are screen-negative (1), detected adenoma (2), screen-detected CRC (3), clinically manifested CRC (3). Three relevant estimands for evaluating this type of overdiagnosis with a natural history model are presented. Then the methods are applied to estimate overdiagnosis proportion in guaiac faecal occult blood test (gFOBT) based CRC screening programme conducted in Finland between 2004 and 2016. The resulting mean overdiagnosis probability for all the patients that had an adenoma detected for programme is 0.48 (0.38, 0.56, 95-percent credible interval). Different estimates for overdiagnosis in sex and age-specific stratas of the screened population are also provided. In addition to these findings, the natural history model can be used to gain more insight about natural progression of colorectal cancer.

Streptococcus pneumoniae is considered to be one of the most common causes of pneumonia and is known to cause a significant disease burden worldwide. During the past two decades much effort has been made globally to prevent pneumococcal illnesses through the use of vaccines. In Finland, all children under the age of five have been eligible to receive pneumococcal conjugate vaccine as part of the national vaccination programme since 2010. The impact of the pneumococcal vaccination has been studied extensively in Finland, and a significant decrease in the incidence of pneumonia has been observed among all vaccine-age children. One research question not yet examined in the previous studies is the exact point of time after which the impact of vaccination can be discerned in the incidence rates. This thesis considers a novel approach to multiple change point detection for time series data, where the change point problem is expressed in the form of a regression model. The model is specified so that potential change point positions are represented as separate explanatory variables. Relevant change points are then chosen by applying several established variable selection methods to the model. Out of these methods, the lasso estimate, its Bayesian analogue and two other Gaussian scale mixture priors are considered in this work. The change point model was implemented with the selected variable selection methods for age-group specific time series of pneumonia incidence rates in Finland between 2001 and 2016 to detect any changes that could be attributed to the introduction of the vaccine. These datasets were produced from routinely generated hospital discharge records, the operationalization of which is also discussed in the thesis. Aside from the vaccinated age group of under five year olds, data for both 25-44 year olds and over 65 year olds were also considered to inspect possible indirect effects of the vaccination. The implementations with different variable selection methods all provided very similar results for each age group. For under five year olds a change point during spring 2011 was selected, while for the over 65 year olds none were chosen during or after the introduction of the vaccine. For 25-44 year olds multiple change points between 2009 and 2014 were selected, but whether any of these could be attributed to the vaccination remains an open question.

The purpose of this thesis is to study the cumulative probability of a false-positive (FP) test result during the Finnish 20-year breast cancer screening program. This study is based on breast cancer screening data provided by the Mass Screening Registry of the Finnish Cancer Registry, which consists of women aged 50–51 years at the time of their first invitation to mammography screening in 1992–1995. Generalized estimating equations (GEE) are used to estimate the cumulative probability of a FP screening result. In the theoretical part we present the corresponding theory together with reviewing the theory of generalized linear models (GLM). The cumulative probabilities are calculated from the modeling of individual examinations by using the theory and formulas of conditional probability. The confidence intervals (Cl) are calculated by using Monte Carlo simulation relying on the asymptotic properties of the GEE estimates. The estimated cumulative risk of at least one FP during the screening program was 15.84% (95% Cl: 15.49–16.18%). Previous FP findings increased the risk of (another) FP results with an odds ratio (OR) of 1.91 (95% Cl: 1.78–2.04), and OR 3.09 (95% Cl: 2.49–3.83) for one or more previous FP results, respectively. Irregular screening attendance increased the risk of FP results with an OR of 1.46 (95% Cl: 1.37–1.56).

When couples with children split or divorce, they are often unable to come to a mutual agreement concerning their child's place of residency, custody, the child's meetings with the other parent and the frequency of these meetings, or financial aid one parent is obliged to pay the other parent for the child. In many countries, these disagreements quite often lead to long disputes in court. A lot of research has been made (both in Finland and internationally) concerning the court's consideration of disputes about children. This thesis studies the disputes on custody and residency of a child in the district courts of Finland. The objective is to find out which factors play the biggest role in solving these disputes in court. Nine district courts of Finland have kindly provided the documents of the disputes concerning custody and residency of children from the period of 2004 - 2015. Only the cases where a dispute was solely between the parents of a child (no other relatives) and where the final decision was made by court (no agreement between the parties) are taken into analysis. Disputes are divided into two types - the ones where residency of a child was involved in a dispute (residency disputes) and the ones where it was not involved (custody disputes). The winner of a dispute is a dependent variable. A logistic regression model is applied for the custody disputes, and a cumulative logistic regression model is applied for the residency disputes. Due to results of the analysis, mothers win more disputes than fathers, but the difference is statistically significant only for the residency disputes. When only father is of a foreign background, it lowers father's winning chances in a custody dispute, but neither father's nor mother's foreign backgrounds are statistically significant for the residency disputes. A substantiated violence of father towards mother again acts negatively for fathers in custody disputes, and so does a non-substantiated accusation regarding alcohol or drug abuse by father. For the residency disputes, the main factors decreasing fathers' probability to win are mother hiring a legal assistant and father receiving legal aid (which takes place when father is not financially capable of hiring a legal assistant). Established conditions of a child at one of the parents increase the winning chances of that parent, but the effect is higher for fathers. All the accusations (both substantiated and non-substantiated in court) act in favor of fathers; these are substantiated mother's mental disorder, non-substantiated alcohol or drug abuse by mother and non-substantiated accusation regarding father's violence towards mother. At the same time, no variables regarding genders of children disputed about, genders of a judge or of legal assistants are statistically significant in the models. The same concerns the parents' demands in court, as well as the ages of parents (and their difference) and of children involved in disputes. This investigation can be extended by adding the disputes from other years and from other district courts into the analysis.

Text mining methods provide a solution to the task of extracting relevant information from large text datasets. These methods can be applied to extract the relevant parts of Suomi24 internet health discussion to analyze how people discuss and negotiate their health through words, which represents medication or symptoms. Semantic similarities between these two concepts can be examined by learning the word vector representations from data and exploring the vector space using Word2Vec, a popular word embedding method. This thesis reviews how the training of word similarity models is affected by increasing corpus size using text retrieval methods.The effects of corpus size are examined by comparing the measured cosine similarity distances between word vectors representations in two different vector spaces. Word vector representations are learned using two different sized corpora. The first corpus includes only messages from the health discussion area of Suomi24. The second corpus includes the same messages as the first corpus, but also messages from other discussion areas, which include health related words. Cosine similarities are evaluated on using concept vocabularies including relevant health related words. Increasing the number of training examples by almost 30% did not have a drastic effect on the qualities of the training data. The results did not indicate a distinct connection between corpus size and the measured cosine similarity distances between word vector representations of health related words.

Failures in the drinking water distribution system often cause gastrointestinal outbreaks associated with multiple pathogens. We investigated a community-wide waterborne outbreak using a polyphasic approach combining advanced epidemiological, statistical, spatial and microbiological methods. A water pipeline breakage due to construction works occurred in the water distribution line in Vuorela, Eastern Finland on July 4th 2012. Two weeks later, gastrointestinal illness in the community increased and immediate control measures were implemented. Of 2931 inhabitants of the defined outbreak area, a total of 473 (16 %) responded to the web-based questionnaire. Samples from patients and water were analyzed for multiple microbial targets, subjected to appropriate molecular typing and microbial community analysis. We developed a method that enabled us to calculate the distance between the water pipe line breakage point and inhabitant locations. We used the responses obtained from the questionnaires in the univariate and multivariate analysis as explanatory variables. In addition, we used spatial logistic regression model to further analyze the data. The main symptoms in the cohort were stomach ache, nausea and diarrhoea. The clinical picture was mild and the length of the illness had a median of three days. Several pathogens and/or faecal indicators were detected by from the patient faecal and/or water samples, including sapovirus, single Campylobacter jejuni, arcobacters and various E. coli types (EHEC, EPEC, EAEC and EHEC). A case definition was created based on the clinical symptoms, which was used as a response variable in the statistical models. Drinking untreated tap water from the defined outbreak area had a risk ratio (proportion of those exposed among ill to those exposed among healthy) of 5.6 (95% CI 1.9-16.4) increasing in a dose response manner. We were able to calculate the distance between the water breakage point and the inhabitant position by the path of the water distribution network with the method developed for this study. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Children were more likely to fall ill. In the multiple log and logistic regression models, age (inversely), distance from the breakage point (inversely) and drinking the tap water were significant. In addition, a spatial term (describing the spread of the infection between close contacts, inaccuracy in the distance variable and nonlinear fluctuation of the water in the distribution network) was significant. Transmission between persons is common among children and with viral infections. The novel methods used in this study improved the characterization of the source of the infections, and aided to define the extent and magnitude of this outbreak. These methods may be applied to wide range of future outbreaks.

Eturauhassyöpä on miesten yleisin syöpä länsimaissa. Suomessa se aiheuttaa vuosittain noin 800 kuolemaa ja uusia eturauhassyöpiä diagnosoidaan vuosittain yli 4 000 kappaletta. Eturauhassyövän tarkasteleminen tilastotieteen keinoin on lähtökohtaisesti kannattavaa, koska eturauhassyövän diagnosoinnista ja hoidosta kerätään laajoja ja kattavia tietoaineistoja. Aineistojen otoskoot ovat lisäksi sairauden yleisyydestä johtuen verrattain suuria, mikä edesauttaa tilastollisia analyyseja. Tässä tutkielmassa analysoidaan tilastollisesti suomalaisesta eturauhassyövän seulontatutkimuksesta peräisin olevaa aineistoa ($n$=1 608) elinaika-analyysin keinoin. Elinaika-analyysi on tilastotieteen osa-alue, jonka tehtävänä on antaa vastauksia kysymyksiin, jotka liittyvät tiettyjen kiinnostavien tapahtumien ilmenemiseen ja selittävien tekijöiden vaikutukseen tapahtumien ilmenemiseen. Elinaika-analyysin menetelmistä sovelletaan regressiomalleihin kuuluvaa Coxin suhteellisten riskitiheyksien mallia ja Kaplan-Meierin estimaattoreita. Sovelletut menetelmät ovat yleisesti sovellettuja eturauhassyövän tilastollisessa analyysissa. Analyysin tulokset osoittavat, että diagnoosihetkellä määritetyt muuttujat selittävät eturauhassyöpäkuolleisuutta tilastollisesti merkitsevällä tavalla. Merkittävä osa kuolleisuudesta jää kuitenkin selittämättä, minkä vuoksi saatujen tulosten sovellettavuus rajoittuu väestötason tarkasteluun; yksittäisiä potilaita koskevien hoitopäätösten teko vaatii diagnoosihetkellä määritettyjen muuttujien lisäksi muita tutkimuksia. Analyysiin sisältyvät diagnostiset tarkastelut eivät paljasta merkittäviä poikkeamia sovellettuihin tilastollisiin menetelmiin sisältyvistä oletuksista.