1 / 67

WEATHER TYPE CLASSIFICATIONS IN HUMAN HEALTH STUDIES

WEATHER TYPE CLASSIFICATIONS IN HUMAN HEALTH STUDIES. Applications in South Korea. Jan Kysel ý Institute of Atmospheric Physics , Pra gue Czech Republic with support and inputs from Radan Huth Institute of Atmospheric Physics , Pra gue Jiyoung Kim

vivian
Download Presentation

WEATHER TYPE CLASSIFICATIONS IN HUMAN HEALTH STUDIES

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. WEATHER TYPE CLASSIFICATIONS IN HUMAN HEALTH STUDIES Applications in South Korea Jan Kyselý Institute of Atmospheric Physics, Prague Czech Republic with support and inputs from Radan Huth Institute of Atmospheric Physics, Prague Jiyoung Kim Korea Meteorological Administration, Seoul

  2. WEATHER TYPE CLASSIFICATIONS IN HUMAN HEALTH (?) STUDIES MORTALITY

  3. Population: 47 million (2005), Seoul 10.4 million Area: 99 000 km2

  4. Air temperature, heat index and excessmortality in summer 2004, South Korea

  5. Air temperature, heat index and excessmortality in summer 1994, South Korea +3400 excess deaths; excess mortality in all age groups absence of efficient heat-watch-warning system (HWWS)

  6. Documented large natural disasters affecting Korean Peninsula since 1901 (Kysely and Kim 2009, Climate Research 38:105-116) ~3400 excess deaths represent net excess mortality as no mortality displacement effect appeared after the heat waves

  7. OUTLINE • Introduction to the methodology; the 1994 heat wave in Korea • Data & classification procedure • Results • 3.1 Identification of oppressive air masses • 3.2 Dependence on settings of the classification procedure • 3.3 Selected classifications C6 and C15 • 3.4 Regression models for excess mortality within the oppressive AMs • 4. Concluding remarks • 5. The last slide

  8. The 1994 heat wave summer 1994 ‘usual summer’ (2002)

  9. The 1994 heat wave Excess mortality in individual age groups and genders during the 1994 heat waves

  10. The 1994 heat wave (in a ‘climate change’ perspective) if a gradual warming of 0.04C/year is assumed over the period 2001-2060, the recurrence interval of a very long spell of days with temperature exceeding a high threshold (as in the 1994 heat wave) is estimated to decrease to around 40 (10) years in the 2021-2030 (2041-2050) decade

  11. “Airmass”classifications & mortality • Heat-watch-warning systems (HWWS): • apply methods to determine whether a day will be associated with elevated mortality risks according to weather forecast & take action when oppressive day is predicted • often make use of objective classifications of weather types (‘air masses’, AMs) – take into account the entire weather situation rather than single elements • identify ‘oppressive’ AMs associated with elevated mortality in a given location/area & apply regression models within the oppressive AMs in order to account for (and predict) excess mortality

  12. “Airmass”classifications & mortality The idea behind: human physiology responds to the whole ‘umbrella of air’ and not single weather elements (although there is little doubt that air temperature and humidity are the two most important parameters determining the thermal comfort)

  13. “Airmass”classifications & mortality • Two basic types of classifications: • ‘Temporal Synoptic Index’ (TSI; Kalkstein, 1991; Kalkstein et al., 1996; McGregor, 1999) • ‘Spatial Synoptic Classification’ (SSC; Kalkstein and Greene, 1997; Sheridan, 2002) in both methods, one AM is representative for a given day & location/region under study the classifications are based on a relatively standard set (although differing among studies) of input variables: air temperature (T), humidity variable (e.g. T-Td), total cloud amount (TCA), wind components, and atmospheric pressure • TSI: location-specific AMs produced • SSC: more universal, allows for a comparison between places

  14. “Airmass”classifications & mortality • Methodology: TSI & SSC differ in the statistical approach to the identification of AMs: • TSI – PCA and cluster analysis used to define the AMs SSC – days are assigned to one of several predetermined types (seed days) (straightforward interpretation of the SSC over larger areas compensate for its drawback that the representative days must be identified manually, involving a large degree of subjectivity) • Focus on TSI in this presentation (SSC “for comparison”): (i) no need to make regional- or continental-scale comparisons of the AMs and their links to human health (ii) avoid the subjectivity involved in the initial step of the SSC, i.e. the predetermination of the AMs

  15. “Airmass”classifications & mortality • both TSI and SSC utilized to a comparable extent in previous studies • applications: • impacts of weather conditions on mortality • impacts on hospital admissions • mostly summer, but winter also examined • total mortality of all causes usually found the most useful and reliable characteristic of human health effects • the geographic range: North America (US, Canada), Europe (UK, France, Italy, Greece, Czech Republic), Asia (China, Korea, Japan), Australia • the development of HWWS (based on TSI – Kalkstein et al. 1996, or SSC – Sheridan and Kalkstein 2004) •  the AM classifications have become a sort of ‘standard tools’ in biometeorological studies

  16. “Airmass”classifications & mortality • TSI: individual studies differ in many specific ‘settings’ of the classification procedure, including • the set of input variables, • the clustering algorithm, • the number of clusters (AMs) formed the role of these settings and choices on results usually not discussed • ! often no reasoning or justification of the settings ! • important details of the classification which are needed for its ‘reproduction’ are missing (only 3 out of 11 studies on TSI specify the number of PCs retained; some studies do not present the clustering algorithm and/or even the number of AMs formed)

  17. Kyselý et al., International Journal of Climatology 2009

  18. Kyselý et al., International Journal of Climatology 2009

  19. Main questions • To what extent do results (i.e. the AMs formed and their links to human mortality) depend on the settings of the classification procedure? • the selection of input meteorological variables • the way the input variables are treated (averaged/pooled station data) • the number of PCs retained for the cluster analysis • the number of clusters (AMs) formed • Which classification is most useful for a possible application in HWWS?

  20. Introduction • Data & classification procedure • Results • 3.1 Identification of oppressive air masses • 3.2 Dependence on settings of the classification procedure • 3.3 Selected classifications C6 and C15 • 3.4 Regression models for excess mortality within the oppressive AMs • 4. Concluding remarks • 5. The last slide

  21. Data & classification procedure Daily mortality data: 1991-2005 total (all-cause) mortality Excess daily mortality: deviations of the observed number of deaths from expected (baseline) number of deaths Expected (baseline) number of deaths takes into account long-term changes in mortality, the seasonal and the weekly cycles excess mortality examined in the whole population (all ages) and the elderly (persons aged 70+ years) several confounding factors controlled for e.g. days with very large accidents (aviation and maritime disasters, a store collapse, …) and death tolls due to severe natural disasters (typhoons and floods), resulting in more than 100 accidental or disaster-related deaths each

  22. Long-term changes in mortality ( standardization) BUT also seasonal and weekly cycles…

  23. Data & classification procedure • Input meteorological data: • air temperature (T), dew-point deficit (T-Td), zonal wind, meridional wind and total cloud amount (TCA) • 10 stations representative for the area of South Korea, 4 times a day (3, 9, 15 and 21 LT) • mid-May to mid-September • AM classifications differ in the way the station data are taken into account: the input variables originate from •  the average series for the area, •  the pooled series at the 10 stations considered together

  24. Data & classification procedure • AM classification methodology: • STEP 1 • unrotated PCA to form a set of new orthogonal variables (PCs) • the number of PCs retained conforms to the criteria recommended in literature (a large gap in explained variance; e.g. Richman 1986) • more than one solution for the number of PCs possible in most cases, so time series of different numbers of PCs enter the cluster analysis • STEP 2 • cluster analysis: non-hierarchical k-means method (more useful for the identification of oppressive AMs than hierarchical average linkage clustering) • the number of clusters: 6 (‘small’), 10 (‘moderate’) and 15 (‘large’) (a range around values appearing in literature is spanned; no objective method to determine the number of clusters was used: the data are not clearly structured and form rather a continuum than a set of well-defined separate states)

  25. Data & classification procedure STEP 3 • oppressive AMs: those associated with mean excess mortality significantly different from 0 (t-test) & the mean increase at least 3% relative to the baseline mortality (~ about 20 excess deaths in Korea)

  26. Data & classification procedure • Regression models for predicting excess mortality within the oppressive AM: • STEP 4 • to evaluate the impact of within-AM variations in meteorological elements, a stepwise multiple regression analysis performed on all days classified with the oppressive AM • dependent variables: relative daily excess mortality in the whole population and the elderly (70+ years) • independent variables: weather elements(T, Td, and heat index measured 4 times a day; daily averages of T, Td, heat index, TCA, and wind speed)& non-meteorological factors(day in sequence; time of season – within-season acclimation to heat; year – long-term changes in vulnerability to heat stress; the numbers of days with the oppressive AM since the beginning of summer and in previous summer – shorter-term and longer-term acclimation to oppressive weather conditions) • meteorological variables lagged by 1 and 2 days (t-1, t-2) and changes over 24h periods (d/dt; cf. McGregor, 1999) also considered as possible predictors

  27. Introduction • Data & classification procedure • Results • 3.1 Identification of oppressive air masses • 3.2 Dependence on settings of the classification procedure • 3.3 Selected classifications C6 and C15 • 3.4 Regression models for excess mortality within the oppressive AMs • 4. Concluding remarks • 5. The last slide

  28. IDENTIFICATION OF OPPRESSIVE AMs • the whole set of input variables (T, T-Td, W and TCA): the solution as to the number of PCs unique and the same for both averaged and pooled input data (4 PCs retained) • the reduced sets of input variables (either TCA or both TCA and W omitted): the number of PCs was ambiguous  two solutions considered for both averaged and pooled data T, T-Td, TCA, W: 4 PCs T, T-Td, W: 3 / 4 PCs T, T-Td: 2 / 3 PCs (avg), 2 / 5 PCs (pooled) • the additional PCs describe mainly diurnal variations and local effects • more PCs means more variance of the input variables explained, at the expense of possibly including too many details that may be irrelevant for the AM definition • the relationship between AMs and mortality is better expressed when fewer PCs are retained

  29. IDENTIFICATION OF OPPRESSIVE AMs Boxplots of relative excess mortality in individual AMs • most classifications identify an oppressive AM with enhanced mortality • the mean relative excess mortality 3-7% for the whole population, up to 9% for the elderly (70+ years) • the mean mortality increases in the age group 70+ years always larger than in the whole population

  30. IDENTIFICATION OF OPPRESSIVE AMs Boxplots of relative excess mortality in individual AMs • mean excess mortality is negative or close to zero in the other non-oppressive AMs  skill of the classification procedure in identifying weather conditions associated with mortality impacts • around a quarter of days classified with the oppressive AM is associated with marked excess mortality of 10% and more above the baseline (more than 60 excess deaths a day!)

  31. IDENTIFICATION OF OPPRESSIVE AMs Boxplots of relative excess mortality in individual AMs • BUT not all days with the oppressive AM have excess mortality •  further analysis into which meteorological and non-meteorological factors may account for the excess deaths is needed • no ‘deficit mortality’ counterpart to the oppressive AM in any classification, i.e. an AM associated with pronounced average deficit mortality

  32. IDENTIFICATION OF OPPRESSIVE AMs Input variables: T, T-Td; pooled data; 2 PCs retained; classification with 6 AMs • weather conditions: the oppressive AM the warmest one, associated with large humidity, weak southern flow and below-average TCA (but not the smallest one among AMs) • if there are >1 oppressive AMs they differ rather in ‘additional’ weather characteristics (mainly zonal and meridional wind) than T and Td

  33. DEPENDENCE ON SETTINGS OF THE CLASSIFICATION • Basic characteristics of the oppressive AM (the relative frequency; the mean relative mortality increase on days with the oppressive AM; and the coverage of days with pronounced excess mortality) differ in individual classifications • Two important criteria that the oppressive AM should meet: • separated from the rest of the sample in terms of mean excess mortality • covers large percentage of days with pronounced excess mortality (the most important criterion for the application into predicting elevated mortality risks – pronounced excess mortality in summer is usually heat-related)

  34. DEPENDENCE ON SETTINGS OF THE CLASSIFICATION Coverage of days with large excess mortality  the less frequent the oppressive AM, the larger the mean excess mortality in the oppressive AM (the AM is better separated from the rest of the sample); however, this is at the expense of the coverage of days with elevated mortality the oppressive AM of the classifications with 6 clusters (if any) is therefore associated with smaller mean excess mortality, but a much higher percentage of days with large excess mortality compared to the classifications with 10 and 15 clusters Mean excess mortality (70+ yrs)

  35. DEPENDENCE ON SETTINGS OF THE CLASSIFICATION • Summary of the findings: • additional variables (W, TCA) do not generally improve the results the effects of wind and cloudiness are of secondary importance • differences between classifications based on averaged and pooled data relatively little consistent, depend on the particular set of input variables • the dependence of results on the number of PCs retained relatively large, particularly for pooled input data; fewer PCs give better results for all 3 classifications with T, T-Td and W based on pooled data: the number of PCs governs not only the mean relative excess mortality in the oppressive AMs but even the number of the oppressive AMs (0, 1, or 2)!!!

  36. DEPENDENCE ON SETTINGS OF THE CLASSIFICATION • Classifications based on (T, T-Td) with pooled input data and 2 retained PCs form the most interesting set: • C15 – oppressive AM with very large mean relative excess mortality (6.7% in the whole population, 8.9% in the 70+ years) • C6 – the oppressive AM has the largest coverage of days with large excess mortality

  37. CLASSIFICATIONS C6 & C15 C6 C15 Boxplots of relative excess mortality in individual AMs

  38. CLASSIFICATIONS C6 & C15 large interannual variations in the occurrence of the oppressive AM: 0 days in 1993 (for both C6 and C15) >30 (10) days in some summers for C6 (C15) maximum: 52 (25) days in summer 1994

  39. CLASSIFICATIONS C6 & C15 within-season variability: maximum in late July or early August no occurrences in May and June

  40. CLASSIFICATIONS C6 & C15 • another specific characteristic = persistence: • the average duration of a spell = 4.7 (2.7) days in C6 (C15), much longer than for any other AM • record-breaking durations of the oppressive AMs: 29 (15) days in C6 (C15)

  41. CLASSIFICATIONS C6 & C15 average mortality impacts in the oppressive AM depend on the day in sequence: mean excess mortality increases with the duration of a spell on the first few days  then a slight decline appears  followed by a sharp increase in the mortality response, mainly in the elderly, during late stages of prolonged occurrences of the oppressive weather (days 15-23 in C6, 9-15 in C15)

  42. CLASSIFICATIONS C6 & C15 heat stress effects tend to cumulate over the first few days with the oppressive weather, a certain degree of short-term acclimation to heat develops after a week or so BUT this acclimation (which may be physiological as well as behavioural) does not play a role anymore if the oppressive weather persists for a very long time

  43. CLASSIFICATIONS C6 & C15 the oppressive AM covers most days with pronounced excess mortality (daily excess mortality exceeds 200 deaths in the peak of the 1994 heat waves!) BUT some days with relatively large excess mortality in 1994, after the peak of the heat wave, are not classified with the oppressive AM in C15  a consequence of the trade-off between the coverage of days with pronounced excess mortality (better in C6) and mean mortality increase on days classified with the AM (better/larger in C15)

  44. CLASSIFICATIONS C6 & C15

  45. CLASSIFICATIONS C6 & C15 the oppressive AM covers a large portion of days with pronounced heat-related mortality in C15, and nearly all in C6 BUT not all days classified with the oppressive AMs associated with excess mortality: the links are complex and mortality is affected not only by meteorological elements but also other factors (timing within a season, timing within a spell of oppressive days, longer-term changes in the public perception of heat, etc.)

  46. REGRESSION MODELS FOR EXCESS MORTALITY STEP 1: linear regression models developed using the step-wise screening and the whole available period of data (1991-2005); BIC used to control for overfitting

  47. REGRESSION MODELS FOR EXCESS MORTALITY • C6: • excess mortality positively associated with day-time temperature (T15) and day-to-day change in night-time dew-point temperature (dTd3) • non-meteorological factors also important: mortality impacts decrease with the number of days with the oppressive AM both in previous summer and since the beginning of summer in a given year • for the whole population, mortality effects are found to decrease over time, too

  48. REGRESSION MODELS FOR EXCESS MORTALITY • C15: • regression models somewhat more complex, with different predictors selected for the whole population and the elderly • two non-meteorological factors are important: excess mortality in the oppressive AM increases with the day in sequence, and decreases with the time of season

  49. REGRESSION MODELS FOR EXCESS MORTALITY larger percentage of explained variance in C15 than C6 (much smaller sample size, 98 vs. 343; possible overfitting in C15, i.e. the models may be too complex for given amount of data)

More Related