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Sweden has a long-standing tradition of recording causes of death, dating back to the establishment of Tabellverket, in 1749. At that time, parish ministers were responsible for documenting causes of death in parish registers and compiling related statistics, which were then used for national population data. This article explores the various agents involved in the development and maintenance of this reporting system. These include government officials (including medical scientists), practising physicians, clergy and the close social networks of the deceased. Each group played a vital role in shaping how causes of death were recorded and understood. The reporting system functioned as an information network, influenced by the differing levels of knowledge and perspectives on disease and mortality held by these agents. While the clergy initially occupied a central position in this system, the role of physicians — limited at first — gradually expanded as medical expertise and authority grew over time. Despite the limitations of the early reporting system, the preserved cause-of-death data offers invaluable insights into the changing landscape of public health, disease patterns and mortality in Sweden from the mid-18th to the mid-20th century.

In this article, we analyse infant mortality in Sundsvall 1860–1892. The focus is on the pattern and development of causes of death with separate analyses of neonatal and post-neonatal mortality. Furthermore, we discuss the development of infant mortality in relation to possible determinants in the historical context in Sundsvall. The results show substantial differences between neonatal and post-neonatal mortality when it comes to causes of death as well as their seasonal pattern. For deaths in the first 28 days, a large proportion of the deaths were diagnosed as unknown disease or given vague and symptom-descriptive diagnoses. For post-neonatal mortality on the other hand, the dominant cause of death categories were water-and food-borne infections and air-borne infections. Water-and food-borne diseases had a very strong seasonal pattern with the peak in late summer — July and August. There is no indication that sanitary improvements in the 1880's led to fewer cases of diarrhoea. Mortality from air-borne diseases on the other hand was lowest during summer, instead peaking in the winter months.

Climate conditions, such as ambient temperatures, play a crucial role in infants' vulnerability to infectious diseases. However, little is known about how climate conditions, such as temperatures and seasonality, affect infectious disease mortality among infants in high mortality settings. The aim of our study was to investigate the association between cause-specific infant mortality and ambient temperatures and seasonality. We applied a retrospective study design using parish register data from Sweden covering the 1868–1892 period in combination with daily temperature data. Mortality due to water- and foodborne diseases, airborne infectious diseases and other causes was modelled as a function of temperature exposure in the previous 14 days using distributed lagged non-linear models. We found that airborne infectious diseasemortality was not related to cold temperatures, but rather to seasonality. The summer peaks in mortality due to water- and foodborne infections were associated with high temperatures, and not with seasonality. The increased vulnerability of infants to infectious diseases at high temperatures is a significant future risk, given that global temperatures are projected to rise in the coming decades.

Climate variability, such as ambient temperature, is crucial for infants' vulnerability to infectious diseases. However, little is known about how climate variability affects infectious disease mortality among infants in high mortality settings. We investigate the association between ambient temperature, seasonality and cause-specific infant mortality. Parish register data from the Sundsvall region in Northern Sweden covering the period 1868-1892 were used in combination with daily temperature data from Härnösand. Mortality due to water- and food-born diseases, airborne infectious diseases, and other causes were modelled as a function of temperature exposure in the previous 14 days using time-series analysis. We found that airborne infectious disease mortality was not related to cold temperatures but rather to seasonality, and that the summer mortality peak due to water- and foodborne infections were associated with high temperatures and not with seasonality. 

The aim of this study was to develop a structure-property model for membrane partitioning of oligopeptides using statistical design methods and multivariate data analysis. A set of 20 tetrapeptides with optional N-methylations at residues 2 and 4 was designed by a D-optimal design procedure. After synthesis and purification, the membrane partitioning abilities of the peptides were tested in two chromatographic systems with phospholipids as the stationary phase: immobilized artificial membrane chromatography (IAM) and immobilized liposome chromatography (ILC). The relationship between these measures and three different sets of calculated descriptors was analyzed by partial least-squares projection to latent structures (PLS). The descriptors used were the molecular surface area, Molsurf parameters, and Volsurf parameters. All three models were of good statistical quality and supported that a large hydrogen-bonding potential and the presence of a negative charge impair membrane partitioning, whereas hydrophobic parameters promote partitioning. The findings are in accordance with what has been found for absorption of known drugs and have implications for the design of peptide-like drugs with good oral bioavailability.

Multivariate design is useful for selecting informative training- and validation sets. The essence of this approach is (i) to describe the compounds with many descriptors, (ii) to summarize these descriptors by means of principal component analysis (PCA), and (iii) to create an informative multivariate design in the established PC-scores ("principal properties", "PPs"). This approach has been used in many areas for selecting representative compounds, e.g., organic chemistry, crystallization modelling, environmental chemistry5and QSAR, combinatorial chemistry, and biopolymer sequence modelling.

Multivariate statistical process control (MSPC) is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an (1) 'intuitive' univariate approach, and (2) with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis (PCA) method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking (SMART) chart, is introduced and used.

In this study 87 amino acids (AA.s) have been characterized by 26 physicochemical descriptor variables. These descriptor variables include experimentally determined retention values in seven thin-layer chromatography (TLC) systems, three nuclear magnetic resonance (NMR) shift variables, and 16 calculated variables, namely six semiempirical molecular orbital indices, total, polar, and nonpolar surface area, van der Waals volume of the side chain, log P, molecular weight, and four indicator variables describing hydrogen bond donor and acceptor properties, and side chain charge. In the present study, the data from a previous characterization of 55 AA.s from our laboratory have been extended with data for 32 additional AA.s and 14 new descriptor variables. The new 32 AA.s were selected to represent both intermediate and more extreme physicochemical properties, compared to the 20 coded AA.s. The new extended and updated principal property scales, the z-scales, were calculated and aligned to previously reported z(old)-scales. The appropriateness of the extended z-scales were validated by the use in quantitative sequence-activity modeling (QSAM) of 89 elastase substrate analogues and in a QSAM of 29 neurotensin analogues.

In this thesis, attention has been focused on the quantitative description of nucleic acids, proteins and peptides. The strategy was to use multivariate chemometrical methods for improving the understanding of the complex structural codes of these kinds of biological molecules. Tools have been developed that enable quantitative modelling of biological molecules, i.e. models based on data that quantitatively describes their properties. The advantage of such models is that they provide interpretations in terms of chemical characteristics for complex features such as similarity, dissimilarity and potency.

By a multivariate physical-chemical characterization of the building blocks of nucleic acids and proteins, i.e. nucleosides and amino acids, descriptive scales have been developed, so called principal properties. The scales give a description of the intrinsic properties of these building blocks. The multivariate characterization results in a multi-property matrix. A principal component analysis of the multi-property matrix gives a small number of latent variables which are considered as the principal properties of the characterized molecules.

The principal property scales may be used for a wide range of different purposes, such as detecting trends and groupings in large sequence data sets, and for analyzing quantitative relationships between structure and function. In statistical experimental design, the descriptors are well suited as design variables to select combinations of amino acids in such a way that they span a wide range of properties.

The use of these principal property descriptors is demonstrated in the quantitative modelling of relationships between structure and activity of various peptide series, DNA-promoters and in the quantitative modelling of transfer ribonucleic acid sequence data (tRNA).

Twenty nucleosides occurring in transfer ribonucleic acid (tRNA) have been characterized using 21 experimentally determined (HPLC, TLC, NMR, etc.) and calculated (log P, van der Waals surface area, ionization potential, etc.) variables. Principal component analysis (PCA) was performed on the data set and four statistically significant components or principal properties (PPs) were extracted. The PPs described 68·4% of the variance in the data. The PP values are discussed in terms of similarity and dissimilarity among the nucleosides. The loading vectors from the PCA are used for an interpretation of the nature of the PP vectors. Application of the PPs in sequence-activity modelling is demonstrated with 25 DNA-promoter sequences originating from E. coli.