The collection of ethnicity information as part of cancer datasets is important for planning services and ensuring equal access, and for epidemiological studies. However, ethnicity has generally not been well recorded in cancer registries in the UK. The aim of this study was to determine the completeness of ethnicity coding in the Thames Cancer Registry (TCR) database and within the Hospital Episode Statistics (HES) data as held by the London Health Observatory, and to investigate factors associated with ethnicity being recorded.Records for 111821 hospital admissions of London residents with a malignant cancer as a primary diagnosis between April 2002 and March 2003 and records for 25581 London residents diagnosed with cancer in 2002 were examined. Data on sex, age, cancer network of residence, deprivation, proportion of non-whites in the local authority population, and site of cancer were available. The proportion of patients in each group with a valid ethnicity code was calculated. In the TCR data proportions were also calculated adjusted for all other variables.Ethnicity was recorded for 90661 (81.1%) of the hospital admissions in the HES data and 5796 (22.7%) patients on the TCR database. Patients resident in areas with a higher proportion of non-white residents and the most deprived populations were more likely to have an ethnic code on the TCR database, though this pattern was not seen in the HES data. Adjustment did not materially affect the association between deprivation and ethnicity being recorded in the TCR data.There was a large difference in completeness of ethnicity between the data sources. In order to improve the level of recording in TCR data there needs to be better recording of ethnicity in sources TCR data collection staff have access to, or use of information from other sources e.g. electronic data feeds from hospitals or pathology laboratories, or HES data itself supplied directly to TCR. Efforts to collect ethnicity data should be encouraged in all healthcare settings. Future research should explore where the difficulties collecting ethnicity information lie, whether with patients, healthcare professionals or the recording procedure, and how such problems can be overcome. Ethnicity is becoming more and more important in understanding the emerging health needs in different communities and to this end the Thames Cancer Registry (TCR) and the London Health Observatory (LHO) are working together looking at ethnicity coding in cancer.Ethnicity information has been collected in Britain at the last two censuses, 1991 and 2001. In 1991 a simple classification with nine entities was used to show ethnicity: White, Black-Caribbean, Black-African, Black-Other, Indian, Pakistani, Bangladeshi, Chinese, and any other ethnic group. For the 2001 census the classification was changed so that White was expanded to White-British, White-Irish and White-Other, while Asian-Other and four Mixed groups were added to create a total of 16 categories. Cancer registries have included ethnicity as an optional data item since 1993. Ethnicity coding was introduced to the NHS in 1995 as part of the Hospital Episode Statistics (HES) data. Unfortunately the availability of ethnicity data in both datasets has not been improving as rapidly as hoped for, due to a multitude of factors e.g. people are reluctant to collect data they do not feel is being utilised.Accurate coding and collecting of ethnicity is important for epidemiological research and for planning services. Following the Race Relations (Amendment) Act in 2000, the NHS developed guidance to ensure different ethnic groups have equal access to services. Certain cancers can be associated with certain ethnic groups, for example, breast cancer in Ashkenazi women  and prosThere has not been much research on ethnicity in this country due to the poor availability of ethnicity information. Studies have generally been focused in particular areas and heavily reliant on name algorithms to determine ethnicity -7. CountThe aim of this joint project was to determine the completeness of ethnicity coding on the TCR database and in the HES data as held at the LHO, and investigate factors associated with the availability of ethnicity data.The TCR dataset for the calendar year 2002 and the HES dataset for the financial year April 2002 to March 2003 were examined. Data on whether ethnicity was recorded, sex, age, cancer network of residence, deprivation, proportion of non-whites in the local authority population, and site of cancer were available. The TCR dataset records individual tumours whilst the HES dataset records inpatient episodes. As the datasets covered different time periods and had different definitions of cancer records, no attempt was made to match the datasets to validate the ethnic code information. Ethnicity was regarded as recorded if it was a valid, non-missing code.The deprivation data was taken from the Index of Multiple Deprivation 2000 income domain . QuintilThe proportion of non-whites in the populations of the local authorities in London was calculated from the ONS Labour Force Survey  where a 2 tests were used to test for heterogeneity.The proportion of patients who had ethnicity recorded was calculated for each variable collected. For the TCR data, logistic regression was then used to fit a fully adjusted model, including sex, age, cancer network of residence, deprivation, proportion of non-whites in the population and site of cancer. Results were then transformed to obtain an adjusted proportion of patients with ethnicity data provided. Tests for trend were done by fitting categorical variables as continuous, and chiTable There were 25581 London residents registered on the TCR database with a malignant cancer  diagnosed in 2002. A total of 22.7% (5796) had a valid ethnicity code  had high proportions of patients with ethnicity recorded Table . As age There were large differences between the availability of ethnicity data in the TCR and HES datasets. A number of factors were associated with the likelihood of having ethnicity recorded. Patients resident in areas with a higher proportion of non-white residents and the most deprived population were more likely to have an ethnic code on the TCR database, though this pattern was not seen with the HES data.Ethnicity information has generally not been well recorded in the UK. However some health data sources have a high level of completeness. The Survey of Prevalent HIV Infections Diagnosed (SOPHID) is a cross-sectional survey of all individuals who have been diagnosed with an HIV infection and attended for HIV related care at an NHS site within a calendar year. Only 5% of these patients seen in London in 2002 did not have ethnicity recorded. .Recording of ethnicity data has been particularly difficult in the TCR database. The proportion of valid ethnic coding on the TCR database for London residents varied by area of residence, deprivation quintile and the proportion of the population that is non-white. The largest variation in ethnic coding in the HES database was between the cancer networks of residence.The difference in proportions of ethnicity data available in the HES and TCR data may have occurred for a number of reasons. The HES data are downloaded from the hospital patient administration system (PAS) which should include ethnicity. The TCR data come from a number of different data sources, most of which do not have ethnicity as part of the dataset. The primary source of TCR data is a mixture of PAS and pathology data with other data added from radiotherapy or chemotherapy clinic notes. However, some fields, such as ethnicity, may not be available to data collection staff, or staff may not be collecting the data item, viewing it as less important than other variables. This needs to be reviewed internally. Data are also obtained directly from death certificates, GP notes, and outpatient notes. Of all the sources of TCR data, the PAS data is the only source likely to contain ethnicity data which is accessible to the TCR data collection staff.The variation in ethnicity coding between cancer networks of residence in the HES data is likely to be due to differences in trust of admission. A study of access to revascularisation in London examined the completeness of ethnicity coding for related episodes and found wide variation between hospital trusts. .As the HES dataset records in-patient episodes, rather than individual patients, some patients will be recorded more than once. If these patients were more likely to have their ethnicity recorded, the results would be affected by selection bias, with HES completeness figures artificially inflated. Although the populations being examined are different, this is unlikely to explain the large differences in results found.In the TCR data the areas with the highest proportion of non-whites in the population were most likely to record ethnicity. Areas with diverse populations may be more aware of the importance of collecting ethnicity information than areas with a large ethnic majority population.Improved recording of ethnicity in sources of data that TCR have access to will improve completeness, as will highlighting the importance of collecting ethnicity to data collection staff. Alternatively information from other sources e.g. electronic data feeds or HES data itself supplied directly to the TCR should increase completeness. Efforts to collect ethnicity data should be encouraged in all healthcare settings. Future research should explore where the difficulties collecting ethnicity information lie, whether with patients, healthcare professionals or the recording procedure, and how such problems can be overcome.The author(s) declare that they have no competing interests.RHJ performed the more detailed TCR analysis, interpreted the data and helped to draft the manuscript.KML was involved in the conception and design of the study, prepared the TCR data, performed the initial TCR analysis and helped to draft the manuscript.JF was involved in the conception and design of the study, interpreted the data and revised the manuscript.DH was involved in the conception and design of the study, prepared the HES data, performed the HES analysis and revised the manuscript.HM was involved in the conception and design of the study, interpreted the data and revised the manuscript.All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Almost 10% of all individuals who are legally blind also have a mobility impairment. The majority of these individuals are dependent on others for mobility. The Smart Power Assistance Module (SPAM) for manual wheelchairs is being developed to provide independent mobility for this population.A prototype of the SPAM has been developed using Yamaha JWII power assist hubs, sonar and infrared rangefinders, and a microprocessor. The prototype limits the user to moving straight forward, straight backward, or turning in place, and increases the resistance of the wheels based on the proximity of obstacles. The result is haptic feedback to the user regarding the environment surrounding the wheelchair.The prototype has been evaluated with four blindfolded able-bodied users and one individual who is blind but not mobility impaired. For all individuals, the prototype reduced the number of collisions on a simple navigation task.The prototype demonstrates the feasibility of providing navigation assistance to manual wheelchair users, but several shortcomings of the system were identified to be addressed in a second generation prototype. In a and a mobility impairment that makes it difficult or impossible to ambulate independently using a white cane, guide dog, or other traditional mobility aid for the visually impaired. The American Federation for the Blind (AFB) has estimated that 9.61% of all individuals who are legally blind also use a wheelchair or scooter, and an additional 5.25% of individuals who have serious difficulties seeing  also use a wheelchair or scooter (see Appendix). A large number of potential users of the SPAM are expected to be elderly, since visual and physical impairments often accompany the natural aging process. In 2000, approximately 13% of the total US population, or an estimated 35 million people, were age 65 or older; with about 2% at least age 85. By 2030, the older population is projected to double, expanding to 70 million. People age 85 and older are the fastest growing segment of the American population and the US Census Bureau estimates that there are now 65,000 centenarians [The user population for the SPAM consists of individuals with both a visual impairment enarians .Currently, the majority of non-ambulatory visually-impaired individuals are seated in a manual wheelchair and pushed by another person . DependiDespite a long history of research in smart power wheelchairs, there are very few smart wheelchairs currently on the market. Two North American companies, Applied AI and ActivMedia, sell smart power wheelchair prototypes for use by researchers, but neither system is intended for use outside of a research lab. The CALL Center of the University of Edinburgh, Scotland, has developed a wheelchair with bump sensors, a single sonar sensor, and the ability to follow tape tracks on the floor for use within a wheeled-mobility training program . The CALOne common feature of all of these smart wheelchairs is that they are based on power wheelchairs. Power wheelchairs are a convenient platform for researchers, but have several disadvantages when compared with manual wheelchairs. In general, manual wheelchairs are lighter and more maneuverable than power wheelchairs, and can be transported in a car. Manual wheelchairs that make use of power assist hubs are heavier than traditional manual wheelchairs, and can be more difficult to disassemble for transport depending on how the hubs are attached to the frames, but still provide many of the advantages of traditional manual wheelchairs.In a search of the literature, only one other smart wheelchair was identified that was based on a manual wheelchair. The Collaborative Wheelchair Assistant  controlsOne of the few products that is commercially-available and accommodates a manual wheelchair is the Wheelchair Pathfinder , a commeThe right side of Figure Several types of sensors have been integrated into the SPAM. These sensors are used for (1) tracking the state of the wheelchair  and (2) locating obstacles in the wheelchair's environment. Obstacles are identified using infrared rangefinders, sonar sensors and bump sensors. The sonar sensors have a maximum range of 3.05 m and a minimum range of 2.54 cm. The advantages of a smaller range are that (1) the frequency of sonar readings is increased and (2) the sonar system is able to detect obstacles that are extremely close to the wheelchair, which is important for passing through doorways. Infrared range finders provide a focused, highly modulated infrared beam, providing absolute ranging based on simple triangulation. The result is an accurate range value between 0.1 and 1.0 meters in a variety of circumstances. The infrared signal functions at extremely steep angles, even exceeding sixty degrees, and does so both indoors and outdoors, even in bright sunlight. The infrared rangefinders and sonar sensors are housed in. 09 m × .06 m × .04 m boxes  or at the same speed and opposite directions (rotating the wheelchair in place). This greatly simplifies the task of avoiding obstacles but limits the wheelchair user's flexibility in choosing paths of travel.The navigation assistance software was written in C and runs on a TattleTale™ (manufactured by Onset Technologies) 8-bit microprocessor. User input  and sensor data are combined into "cases" that are used to make obstacle avoidance decisions. The specific cases that are in use at any one time varies depending on the specific behavior that is desired from the SPAM . No single case can cause the software to prevent both forward/backward movement and turning, but multiple cases can be triggered at once and result in a situation in which the wheelchair will not move in any direction. The motorized hubs can be turned off in these situations, at which point the SPAM behaves like a normal (but heavy) manual wheelchair.Four able-bodied members of the investigative team and an individual who is blind, but does not have a mobility impairment, took part in an evaluation of the SPAM prototype. Approval for this research was obtained from the University of Pittsburgh's Institutional Review Board. All participants used the SPAM to complete the two obstacle courses shown in Figure As shown in Figure As shown in Figure One clear observation from our preliminary evaluations of the SPAM is the distinct difference between able-bodied, but blindfolded, individuals and individuals who are completely blind. The participant who is blind was much better at localizing the sound target and keeping track of his location in the course than any of the able-bodied participants. The blind participant also found it much easier to learn the layout of the course. One possible implication of these results is that the SPAM may be more useful for individuals who are newly visually impaired. Another possible implication is that the SPAM may be very useful in novel or frequently-changing environments, but not particularly useful in well-known, static environments.Our preliminary evaluation of the SPAM demonstrates that the SPAM can increase the safety of visually-impaired manual wheelchair users. Of course, there is a large difference between a constrained laboratory environment and real-world environments, and much additional development and testing remains to be done. Our evaluation also identified several shortcomings. In particular, navigation assistance increased the time required to complete the navigation task. This was the result of an overly conservative obstacle avoidance algorithm, which slowed the SPAM more than necessary.Our ability to control the SPAM was limited by our decision to retain the original electronics of the JWII hubs in place. This greatly simplified the development process, and allowed us to quickly produce a prototype that could be tested. The trade-off, however, was that our microprocessor and control software were not communicating directly with the motors within the hubs but were, instead, communicating with the JWII microprocessor and control software which controlled the motors. The control algorithms built into the JWII acted as a filter that made small adjustments in the speed and direction of the wheelchair difficult. This is why the motion of the SPAM was limited to straight forward, straight backward, and turning in place.One unanticipated benefit of using power assist hubs which emerged during development was the ability to provide "haptic feedback" to the wheelchair user. As the SPAM approaches an obstacle, the hubs provide greater resistance. This allows the user to get an impression of the environment around the wheelchair through a series of forward pushes and rotations in place. In addition to individuals with visual impairments, this haptic feedback may also prove helpful for people with traumatic brain injuries.The lessons learned from the first SPAM prototype are being incorporated into a second generation SPAM prototype (currently under development). Most importantly, the microprocessor used by the JWII hubs is being replaced with a new (programmable) microprocessor, which will allow the SPAM to provide much smoother and more nuanced control of the wheelchair. New enclosures have also been designed for the sensors that provide increased mounting flexibility, and have increased the number of modules. The additional sensor modules have forced us to abandon the case-based approach to obstacle avoidance, and alternative algorithms are being pursued.AT Sciences has applied for a patent for the SPAM. AT Sciences will be submitting a Phase II SBIR proposal to the National Eye Institute based, in part, on the results contained in this manuscript. Dr. Simpson is not employed by, nor does he hold any stocks or shares in, AT Sciences. Dr. Simpson participated in this research through a subcontract negotiated between AT Sciences and the University of Pittsburgh.RS, EL and RC conceived of the project and participated in its design and coordination. RS implemented the obstacle avoidance software, conducted the user trials and drafted the manuscript. SG, DD, SH and WA implemented the hardware for the SPAM, and interfaced the TattleTale microprocessor with the JWII electronics. VS reconstructed the SPAM to complete additional user testing. All authors read and approved the final manuscript.The American Federation for the Blind performed the following analysis using data from the 1994 and 1995 National Health Interview Survey on Disability, Phase I. Analysis used variables for being legally blind (location 422) and having serious difficulty seeing (location 401), using a manual wheelchair (location 524), an electric wheelchair (location 526), or a scooter (location 528), and age recode 2 location 30). Data were extracted from phase I person files for each year, the design variables were recoded, the 2 years of data were combined, and the final weights were adjusted (wfta/2) in order to compute crosstabs in SUDAAN. Among all persons who are legally blind(0. Data w


Describing adverse health effects and identifying vulnerable populations during and after a disaster are important aspects of any disaster relief operation. This study aimed to describe the mortality and related risk factors which affected the displaced population over a period of two and a half months after the 2004 Indian Ocean tsunami in an eastern coastal district of Sri Lanka.A cross-sectional household survey was conducted in 13 evacuation camps for internally displaced persons (IDP). Information on all pre-tsunami family members was collected from householders, and all deaths which occurred during the recall period (77 to 80 days starting from the day of the tsunami) were recorded. The distribution of mortality and associated risk factors were analysed. Logistic regression modelling using the generalized estimating equations method was applied in multivariate analysis.p < 0.001), children and the elderly . Other risk factors, such as being indoors at the time of the tsunami , the house destruction level  and fishing as an occupation  were also significantly associated with increased mortality. These correlations remained significant after adjusting for the confounding effects by multivariate analysis.Overall mortality rate out of 3,533 individuals from 859 households was 12.9% (446 deaths and 11 missing persons). The majority of the deaths occurred during and immediately after the disaster. A higher mortality was observed among females (17.5% vs. 8.2% for males, A significantly high mortality was observed in women and children among the displaced population in the eastern coastal district of Sri Lanka who were examined by us. Reconstruction activities should take into consideration these changes in population structure. On 26 December 2004, the largest earthquake of the past 40 years occurred off the west coast of northern Sumatra and generated the most deadly and devastating tsunami in recorded history ,2. Sri LUnderstanding the impact of a disaster on human health is essential for effective disaster management. Epidemiological methods can be applied in disaster settings to describe the adverse health effects and to analyze the factors associated with them . By idenDisaster epidemiology is a developing discipline, but reports have accumulated on various types of natural disasters such as earthquakes, floods, hurricanes, tornados and famines, as well as wars and other calamities of human origin. However, few epidemiological studies have been conducted on tsunamis, and their health consequences remain poorly documented.We conducted a household survey to examine the mortality among internally displaced persons (IDP) during and after the Indian Ocean Tsunami in Sri Lanka.While our previously published report described the temporal distribution of death, i.e. when people died , this stThe study was based on a cross-sectional household survey of the IDP conducted from 13 to 18 March 2005 in three administrative divisions of Ampara, an eastern coastal district of Sri Lanka. The mortality, the primary outcome measurement, was analysed as a retrospective cohort study.The study protocol was reviewed and approved by the Ethical Review Committee, Faculty of Medicine, University of Peradeniya.The IDP were highly mobile, and some frequently relocated from one camp to another, especially when school buildings were vacated for the resumption of classes. In view of these difficulties in defining the sampling framework, we decided to include all camp residents in our study sample without any selection, i.e. all households in the 13 surveyed camps were invited to participate in the study.To define the household, we used a household registration card that was held by the householder. The local government issued the registration card for each affected family; it contains basic demographic information on all family members. The displaced people tended to keep the card securely because it was particularly important for receiving food rations and other aid supplies.We invited all households in each camp to participate in the study and aimed at interviewing the householder as defined on the registration card. If the householder was not available for the interview, a proxy informant aged more than 15 years in the family  was invited. Either the householder or the proxy informant provided all information on the household and family members. There was no refusal among 859 invited households.After giving written informed consent, the informants were interviewed by locally employed surveyors with a pre-piloted, structured questionnaire. Information on all family members as of 25 December 2004 (the day before the tsunami) was collected from the informant along with household information such as the area of residence, ethnicity, religion, occupation, household income and educational level. If any death occurred in the family between 26 December 2004 and the day of the survey, the informant was asked to recall the date and cause of the death. Other individual information included morbidity before the tsunami, age, sex, and the individual's location at the time of the tsunami.To improve the accuracy of the demographic data, we verified the information on the family members by referring to the household registration card and the correction was made accordingly during the interview.The sample size was calculated at 5% significant level and 90% power in order to detect the difference in mortality between 5% and 10% for males and females, based on the findings of our pilot study. Under the assumption of an intra-cluster (household) correlation of 0.5 and an average of four members per household, at least 777 households were needed for the analysis. Thus, we aimed at the collection of data on 800 households.The data were entered in a relational database developed on EpiData 3.1 , in which a series of checking syntaxes was incorporated to detect data inconsistencies. The cleaned dataset was transferred to Stata 8.0  for statistical analysis.The sample was described initially by simple tabulations, followed by a crude analysis of the stratum-specific mortality and the odds ratio of death for each explanatory variable. The adjusted odds ratios were calculated by logistic regression modelling in order to control for the confounding effects between the explanatory variables. The variables were entered into the model one by one in order of strength of the effect on mortality. The model was evaluated at each addition of an explanatory variable by the likelihood ratio test against its preceding model. If the likelihood ratio test did not reveal any significant difference between the two models, the additional variable was removed from the model. Thus, the final model was established when the predictive capacity of the model would not improve further by entering any more variables.Our individual observations were not likely to be independent because of the household-level sampling. In order to take into account the within-household correlation, the generalized estimating equations (GEE) method  was applThe characteristics of the study sample are summarised in Table Overall, 446 deaths (12.6%) and 11 missing persons (0.3%) were reported. Despite the relatively long recall period (77 to 80 days), the mortality was concentrated within a few days after the disaster. Among 457 events of deaths or missing persons, 374 (82%) occurred on the day of the tsunami and the remaining 72 (18%) occurred in the following 7 days. No death was reported for more than two months thereafter [The age-specific mortality is presented for males and females separately in Figure In order to identify the risk factors of mortality, the stratum-specific mortality and the crude odds ratio for each explanatory variable were calculated, as shown in Table The location of individuals at the time of the tsunami was significantly associated with mortality. In general, individuals inside buildings had a significantly higher mortality than those outdoors at the time. Even compared with those who were on the beach or in the sea, people who were at home were more likely to die. Pre-existing health problems, the area of residence, the living period and the ethnic background were not associated with mortality.The mortality tended to decrease the higher the educational level , but this result did not apply to households with no school education, which showed low mortality. Among the various categories of occupation, only fishing showed a significantly higher mortality than the other occupations (OR = 1.44 (1.18–1.75) compare to all other occupations). Although the household income varied from 0 to more than 15,000 rupees (approximately 150 USD) per month, there was no evidence of an association between income and mortality. As expected, the level of house destruction correlated linearly with mortality; a significantly high mortality was observed among people whose houses were totally destroyed as compared to the minimum destruction level (OR (CI) = 3.43 (1.74–6.75)).It is likely that some of the identified risk factors were interrelated. For example, the increased risk associated with being indoors may have been confounded by age and sex because women and children were more likely to be in their houses on the morning of the tsunami. In order to control for the confounding effects between the explanatory variables, the adjusted odds ratios were obtained by a logistic regression model according to the procedure described in the previous section. For two variables, the location at the time of the tsunami and the occupation of the householder, the categories were aggregated into dichotomous values for the purpose of modelling. The final model included age, sex, location at the time of the tsunami, educational level, occupation, and house destruction level. The GEE method was applied to the final model to account for within-household correlation. As shown in Table In conclusion, women, children and the elderly were found to comprise the most vulnerable population. The location indoors and the level of house destruction were also significantly associated with mortality. Among the socio-economic measurements, fishing as the household occupation and the educational level of householders showed some evidence of association while, notably, household income was not associated with mortality.The limitations of our study include potential selection bias and the lack of a population-based estimate due to the sampling from IDP camps. Although we identified all pre-tsunami family members by interviewing the householders, we examined only the households that had been accommodated in the IDP camps. Thus, the mortality presented in this study is probably higher than the population-based statistics that include people who did not take refuge in the IDP camps but stayed in their own houses after the tsunami, which obviously would have suffered less destruction.It is also possible that our sample population is an even more vulnerable group among all of the IDP affected by the tsunami. This is because by the time the survey was conducted, relatively affluent families might have moved out from IDP camps and resettled in other locations such as a relative's house. If it is the case, the variation of the exposure measurements were likely to be reduced in our study sample, potentially leading to the decreased effects of the risk factors on mortality. Nevertheless, considering the fact that our sample preserved its population structure representing the national statistics, we believe that our study population is still useful for identifying the risk factors of mortality especially for demographic factors.We observed unexpected findings for some socio-economic variables. Decreased mortality was observed among families with the lowest educational level and apparently there was no association between household income levels and mortality. It has been generally recognized that various socio-economical indicators are the important determinants in predicting who are the most vulnerable during disasters. A question may be raised as to whether we can conclude that our results are inconsistent with this general perception; possibly reflecting the non-discriminative nature of the tsunami disaster. However, we cannot draw a conclusive statement from our study because of the limitations set-out below.There might have been some residual confounding effects behind some of the associations which were not totally controlled. For example, the families in the lowest educational category tended to have a higher age distribution of the family members, which probably reflects the long-term improvement of school enrolment rate over the past decades in Sri Lanka. Because high mortality was observed among young children aged less than 10 years, families whose members were older in age and often without small children, might have experienced decreased mortality.Although we have conducted a multivariate analysis, it is possible that the confounding effect by age was not totally controlled because of the difference in the measurement level .Another important limitation could be the lack of enough variation in exposures due to the sampling from possibly the most vulnerable IDP as noted above. Although the income level ranged from zero to more than 15,000, the majority falls into the categories of 1–2999 and 3000–5999 Sri Lankan Rupees and few households were in the highest category. This distribution of income level seems low comparing previous available data. According to the Household Income and Expenditure Survey 2002/3 by Department of Census and Statistics, the median and mean household income for the Eastern Province (where our study area is located) were 5500 and 7640 respectively and around 20% of the sample households reported more than 10,000 Sri Lankan Rupees of income.Therefore, it is possible that our sample population was a somewhat homogeneous population from relatively low socio-economic groups. This meant that the effect of socio-economic indicators on mortality did not appear to be substantial, i.e. there were reduced effects due to the lack of variation in exposures. Considering these points, our study could not provide a decisive conclusion on the correlation between some socio-economic indicators and mortality.This study revealed a significantly high mortality rate in women, children and the elderly. A similar gender-age mortality pattern has been reported in other disaster settings. In both the 1999 Taiwan earthquake ,8 and thBy contrast, previous studies on floods have revealed clearly different mortality patterns. In an extensive study on flood-related deaths in Europe and the United States, middle-aged men were found to be the most vulnerable population . A studyThe findings of our study indicate that the age-gender mortality pattern of the tsunami victims is similar to that of earthquakes but very different to that of floods. This clear difference in the high-risk groups observed in different types of disasters highlights the need for epidemiological studies to precisely identify the vulnerable groups unique to each disaster setting, and to provide information for subsequent disaster management and planning. We believe that our study contributes to the epidemiological evidence on tsunami-related mortality, which has not been well described to date.In planning reconstruction aid activities, the impact of the change in population structure on the affected community should be thoroughly considered. The loss of the middle-aged female population may have a particularly significant impact, because these individuals are the primary caretakers of their children and families, as highlighted by the officials from WHO and UNICEF . The nutThe consequences of disasters are very complex. Disaster epidemiology can play an important role in describing the adverse health effects and identifying the vulnerable groups during and after disasters. It enables relief and reconstruction activities to be more focused, relevant and efficient. Continuing research is needed to monitor and evaluate the long-term impact of the tsunami disaster on human health.A significantly high mortality was observed in women and children among the displaced population of the eastern coastal district of Sri Lanka who were examined by us. The loss of the middle-aged female population may pose further risks of morbidity, including malnutrition, in the surviving children. Aid agencies and the government may need to take the change in population structure into account in order to deliver reconstruction aid which is relevant to the local social situation and context.The author(s) declare that they have no competing interests.TA, NN and OK designed and coordinated the study and NN is the guarantor. TA, DGMC and SDD conducted and supervised the filed study. TA, NN and KM analysed and interpreted the data. NN developed the draft and the revisions were made by all authors. All authors approved the final version.The pre-publication history for this paper can be accessed here:


Choosing an adequate measurement instrument depends on the proposed use of the instrument, the concept to be measured, the measurement properties , the requirements, the burden for subjects, and costs of the available instruments. As far as measurement properties are concerned, there are no sufficiently specific standards for the evaluation of measurement properties of instruments to measure health status, and also no explicit criteria for what constitutes good measurement properties. In this paper we describe the protocol for the COSMIN study, the objective of which is to develop a checklist that contains COnsensus-based Standards for the selection of health Measurement INstruments, including explicit criteria for satisfying these standards. We will focus on evaluative health related patient-reported outcomes (HR-PROs), i.e. patient-reported health measurement instruments used in a longitudinal design as an outcome measure, excluding health care related PROs, such as satisfaction with care or adherence. The COSMIN standards will be made available in the form of an easily applicable checklist.An international Delphi study will be performed to reach consensus on which and how measurement properties should be assessed, and on criteria for good measurement properties. Two sources of input will be used for the Delphi study: (1) a systematic review of properties, standards and criteria of measurement properties found in systematic reviews of measurement instruments, and (2) an additional literature search of methodological articles presenting a comprehensive checklist of standards and criteria. The Delphi study will consist of four (written) Delphi rounds, with approximately 30 expert panel members with different backgrounds in clinical medicine, biostatistics, psychology, and epidemiology. The final checklist will subsequently be field-tested by assessing the inter-rater reproducibility of the checklist.Since the study will mainly be anonymous, problems that are commonly encountered in face-to-face group meetings, such as the dominance of certain persons in the communication process, will be avoided. By performing a Delphi study and involving many experts, the likelihood that the checklist will have sufficient credibility to be accepted and implemented will increase. Choosing the appropriate health status measurement instrument for a specific purpose is a difficult and time-consuming task. The choice depends on the proposed use of the instrument, the concept to be measured, the readability of the questions, the requirements and costs associated with the use of the instrument, the burden on the subjects, and, last but not least, the measurement properties of the instruments. The measurement properties concern internal consistency, reproducibility, content and construct validity, responsiveness, and interpretability. Kirshner and Guyatt distinguished three kinds of health status measures, i.e. discriminative, predictive and evaluative measures . Not allAlthough there is consensus that measurement instruments must have good measurement properties, only general guidelines are available, and there are no explicit and comprehensive criteria for what constitutes good measurement properties. Without clear standards the evidence-based selection of measurement instruments is strongly hampered.Several authors have suggested standards for the development and evaluation of instruments to measure health status. One of the most elaborate lists was proposed by the Scientific Advisory Committee (SAC) of the Medical Outcomes Trust ,3. The SHowever, often lacking in these standards are explicit criteria for what constitutes good measurement properties. For example, an intraclass correlation coefficient (ICC) has often been recommended as the most appropriate measure of reliability. There have been some suggestions about what constitutes a minimal ICC for good reliability, but it is unclear whether this concerns the point estimate or the lower limit of the confidence interval, and whether a minimal sample size is required. For the assessment of construct validity it is often recommended that explicit hypotheses about the expected results should be tested . HoweverThis has often resulted in a battery of change coefficients being applied to the same data. A possible explanation for this is that often investigators have not conceptualized and declared the anticipated change characteristic  of the sample .With the rapid increase in the number of instruments that are being developed to measure health status there is also an increase in the publication of systematic reviews in which the measurement properties of these instruments are evaluated and compared. These systematic reviews are important tools for the evidence-based selection of instruments. These reviews focus for instance on outcome measures in specific patient groups -16, on iWe recently developed a checklist for the evaluation and comparison of the measurement properties of instruments to measure health status , which wIn this paper we describe the protocol for the COSMIN study. The aim of this study is to develop COnsensus-based Standards for the selection of health Measurement INstruments. Firstly, we will focus on a homogeneous set of measurement instruments, since it is not clear if these standards and criteria can be applied to all sorts of measurement instruments that measure health status. The initial focus of these standards will be on evaluative health related patient-reported outcomes (HR-PROs). We defined evaluative as instruments which are applied to measure HR-PROs in a longitudinal study to assess change over time. With this definition we exclude measurement instruments which are (1) only used as discriminative instruments, (2) only used for predictive purposes, such as diagnostic or screening instruments, or (3) only used as an independent (prognostic) variable, such as a determinant, confounder or effect modifier in a longitudinal study design. PROs include any endpoint derived from patient reports, whether collected in the clinic, in a diary, or by other means, including single-item outcome measures, event logs, symptom reports, formal instruments to measure health-related quality of life (HRQL), health status, adherence, and satisfaction with treatment . By the Note that one and the same measurement instrument can be used for different purposes, such as discriminative, evaluative and predictive purposes. The COSMIN standards focus on the evaluative application of measurement instruments.The COSMIN standards will be made available in the form of an easily applicable checklist. This project consists of the preparation by performing a systematic review and a additional literature search, an international Delphi procedure, and field-testing of the resulting checklist.The aim of the COSMIN study is to develop consensus-based standards for the assessment of evaluative HR-PROs, including explicit criteria for good measurement properties. To develop these standards, the following research questions will be addressed:1. Which measurements properties should be included in the assessment of evaluative HR-PROs, and how should they be defined?2. How should these measurement properties be assessed in terms of study design and statistical analysis? (i.e. standards)3. Which criteria should be applied to define what good measurement properties are?The Delphi procedure is basically a series of sequential questionnaires or 'rounds', interspersed by controlled feedback, that seeks to achieve consensus of opinion among a panel of experts . The DelTo prepare the questionnaires for the Delphi procedure, a systematic literature review will be performed to search for systematic reviews of evaluative health status measurement instruments that describe measurement properties, quality standards and criteria. To be able to find as many as possible standards and criteria we did not yet use the narrow concept of only HR-PROs. Health status measurement instruments include HR-PROs, performance based measures, clinical ratings, etc. We will search in PubMed, Embase and Psycinfo to find systematic reviews of health status measurement instruments. Articles will be included that meet the following inclusion criteria: (1) 'systematic review', (2) the purpose of the review is to find systematically all available 'health status instruments' regarding a specific topic or a specific population, i.e. all questionnaires or performance based measures, etc. or a combination of any of these, (3) the health status instrument has to be applicable as an evaluative measure (i.e. not discriminative or predictive (i.e. diagnostic or screening)), and (4) the purpose of the review is to report on the clinimetric properties of the measurement instruments. Systematic reviews of diagnostic or screening instruments will be excluded. Two of the authors (LM en CT) will perform the selection of articles independently, based on abstracts and if necessary on full text.An additional literature search in PubMed, Embase and Psycinfo of methodological articles and textbooks presenting comprehensive checklists for standards and criteria will be carried out. The purpose of both the review and the additional literature search is to determine which measurement properties are reported in systematic reviews and in existing standards published in methodological papers and textbooks for the evaluation of health status measurement instruments, which measurement properties should be assessed and how (standards), and which criteria are used to define good measurement properties.In the Delphi procedure of the COSMIN study, four Delphi rounds are planned, as outlined in Figure The first round will focus on (1) agreement on which measurement properties of HR-PROs should be assessed, and how these measurement properties should be defined. Subsequent rounds will focus on (2) how the selected measurement properties should be assessed (standards), and on (3) defining criteria for good measurement properties.Each of these three subjects will be at issue in at least two subsequent rounds, in order to offer panel members the opportunity to reconsider and, if appropriate, to change their previous opinion in light of the anonymous responses and considerations of the other panel members. Each subsequent questionnaire contains also a feedback report.In a Delphi procedure the panel members are carefully selected for their knowledge and interest in a specific field. The panel members will be selected by the Steering Committee , based on the following inclusion criteria: they should be experts on the development and evaluation of health status measurement instruments, and should have credibility according to the target audience, indicated by authorship of multiple frequently cited publications on (the methodology of) this subject in important journals, such as Quality of Life Research, the Journal of Clinical Epidemiology, Medical Care, and BioMed Central Medical Research Methodology.Panel members with the following scientific backgrounds will be selected: clinical medicine, biostatistics, psychology (psychometrics), and epidemiology (clinimetrics). They will also be selected to represent important organizations, and to facilitate dissemination and implementation of the checklist, i.e. members of the International Society for Quality of Life (ISOQOL), the Mapi Research Institute, Cochrane PRO methods group, the Patient-Reported Outcomes Group and the European Research Group on Health Outcomes (ERGHO), and editors of important relevant journals, such as Quality of Life Research, and the Journal of Clinical Epidemiology.Approximately 30 panel members (6–7 per category of scientific background) will be considered appropriate . Based oTo reach consensus on each of the three issues outlined above, the same strategy will be carried out in each round. In the first questionnaire the panel members will be asked to rate how strongly they (dis)agree to include each measurement property in the checklist , and what the most appropriate definition for each measurement property is. Ratings will be scored on a 5-point Likert scale (ranging from strongly agree to strongly disagree). The panel members will be asked to give considerations and arguments to support their opinion. They will also be given the opportunity to suggest alternative wordings, to suggest additional measurement properties, or to make any other comments.In the second questionnaire the aim is to reach consensus on the items included in the first questionnaire, i.e. to reach consensus on which relevant measurement properties should be included in the COSMIN checklist, and on their definition. An anonymous feedback report of the results of the first round will be distributed among the panel members with the second questionnaire. The results of Delphi round 1 will be presented both quantitatively (the distribution and mean (or median) scores on the 5-point Likert scales) and qualitatively (the suggestions and comments of the panel members concerning each measurement property and the definitions). The panel members will again be asked to give their opinion on each item. In principle, only measurement properties for which minimal 67% scored at least 3 points in the second round will be selected for inclusion, but the Steering Committee has the right to make alternative decisions after reviewing the responses. If different terminology is proposed by certain panel members, the Steering Committee will choose one term, and will provide a description of synonyms. The decisions made by the Steering Committee will be presented and justified in the feedback report between the Delphi rounds. The panel members will be given the opportunity to react to, and (dis)approve these decisions.This procedure will be repeated for each issue, i.e. which measurement properties should be assessed, how these properties should be assessed, and which criteria should be used to define what good measurement properties.The Steering Committee will decide whether or not consensus was reached. In general, consensus will be defined as "a general agreement among a substantial majority  of panel members". It is expected that it will be possible to reach consensus on which measurement properties should be assessed and how they should be assessed. However, we anticipate that it will be much more difficult to reach consensus on the criteria for good measurement properties, because this often depends on the situation in which the criteria will be applied. A possible outcome of our study will therefore be a checklist with consensus-based standards for the evaluation of the measurement properties of HR-PROs, but with no explicit criteria for good measurement properties for all the measurement properties.After the four Delphi rounds, a first version of the checklist and a user's manual will be prepared by the Steering Committee. This checklist will be tested in a inter-rater reproducibility study, in which a number of raters will be asked to judge a selection of validation studies of a variety of measurement instruments. Inter-rater variability will be determined on each item of the checklist.The Delphi procedure is particularly suitable for the development of consensus-based standards for the evaluation of HR-PROs for several reasons:- A Delphi approach is especially useful for situations in which there is a lack of empirical evidence and decisive factors are rather subjective, and not knowledge based.- It is useful for situations in which strong differences of opinion, e.g. due to differences in expertise and scientific backgrounds, as is anticipated in this study.- The checklist can be developed in co-operation with many experts in the field of health status measurement from different scientific backgrounds and from different countries. By providing feedback from previous rounds, the Delphi technique provides the advantage of a group process of building on the work and expertise of all panel members.- The Delphi technique avoids problems that are commonly encountered in face-to-face group meetings, such as the dominance of certain persons in the communication process, and the geographical constraints and expenses of bringing together a group of experts. The panel members can be kept unaware of the identity and opinions of the other panel members, which allows them to express their personal views freely.- Checklists developed by individual experts or small research groups from one institute, such as our checklist , do not The purpose of the COSMIN study is to develop consensus-based standards to assess the quality of evaluative HR-PROs, so that in the future this can be assessed more uniformly. The checklist (containing these standards) can be used by researchers, reviewers of journals or professionals for the development, evaluation and selection of measurement instruments, the planning of validation studies, and the critical appraisal of them. Our standards should contribute to the improvement of the quality of  HR-PROs.We expect the final version of the checklist to be ready in 2008/2009.The author(s) declare that they have no competing interest.LM is the principal investigator of the study described in this article. CT, DK, LB and HdV developed the initial study protocol. All authors participated in the design and preparation of the study. LM wrote the first draft of the manuscript. All other authors commented on this draft and contributed to the final manuscript.The pre-publication history for this paper can be accessed here:


Although physiotherapy (PT) plays an important role in improving activities of daily living (ADL functioning) and discharge rates, it is unclear how many nursing home residents receive treatment. Furthermore, there is a lack of insight into the determinants that influence the decision for treatment. In this study, we investigated how many nursing home residents receive PT. In addition, we analysed the factors that contribute to the variation in the provision of PT both between nursing homes and between residents.A random sample of 600 elderly residents was taken from a random sample of 15 nursing homes. Residents had to be admitted for rehabilitation or for long-term care. Data were collected through interviews with the nursing home physician and the physiotherapist. Multilevel analysis was used to define the variation in the provision of PT and the factors that are associated with the question whether a resident receives PT or not. Furthermore the amount of PT provided was analysed and the factors that are associated with this.On average 69% of the residents received PT. The percentage of patients receiving treatment differed significantly across nursing homes, and especially the number of physiotherapists available, explained this difference between nursing homes. Residents admitted to a somatic ward for rehabilitation, and male residents in general, were most likely to receive PT. Residents who were treated by a physiotherapist received on average 55 minutes (sd 41) treatment a week. Residents admitted for rehabilitation received more PT a week, as were residents with a status after a total hip replacement.PT is most likely to be provided to residents on a somatic ward, recently admitted for rehabilitation to a nursing home, which has a relatively large number of physiotherapists. This suggests a potential under-use of PT for long-term residents with cognitive problems. It is recommended that physiotherapists reconsider which residents may benefit from treatment. This may require a shift in the focus of physiotherapists from 'recovery and discharge' to 'quality of life and well-being'. Approximately 3% of people aged 65 years and older live in one of the Netherlands' 334 nursing homes . Dutch nFurthermore, it is not only the number of residents receiving allied health care that is unknown. There is also a lack of insight into the determinants that influence the decision to treat, or explain the amount of care provided to patients in the nursing home. Several studies found that payment incentives were associated with the likelihood of receiving PT and OT, as well as with the total amount of treatment given -20. OtheUntil now, there is little information about how many, or which, residents receive PT in Dutch nursing homes. We expect that prevalence rates for rehabilitation services will vary across different nursing homes, as there is a lack of nationally accepted standards or criteria, on which to base decisions regarding the provision of treatment. Although health-care provision should be based on residents' needs, we hypothesise that the provision of treatment is associated with the characteristics of the residents, for example need for care or age, and too with characteristics of the nursing home, for example the type of nursing home and the supply of care.Therefore, the aim of the study is twofold. Firstly, to study the variation in the provision of PT among nursing homes and the factors that are associated with this variation. Secondly, to study the variation in the amount of PT both among nursing homes and among residents, and to identify factors that are associated with this variation.A cross-sectional design was used. Randomisation took place on two levels. Firstly, a random and weighted sample of 15 nursing homes was obtained from a list of all Dutch nursing homes . These 1Secondly, from these 15 nursing homes a random and weighted sample of 600 residents was taken, representing 1 % of all 51.174 beds available in Dutch (somatic and combined) nursing homes . The resData from these residents were collected through interviews with the nursing home physician and the physiotherapist assigned to the resident. As part of these highly structured interviews, a questionnaire was filled out by the researcher for each resident, in the presence of the physician and therapist, enabling them to consult the medical file of the resident simultaneously. The domains in the questionnaire were based on the Minimum Data Set of the Residents Assessment Instrument (RAI) and deteTwo outcome, or dependent, variables were used. The first, dichotomous, outcome variable was whether the resident had received PT in the six months preceding the interview. All therapeutic intervention as registered in the medical file, was designated as treatment given, without time restrictions on the amount of treatment per week. The second, continuous, outcome variable was the amount of PT provided to the resident during the last 6 months, estimated by the PT in minutes per week.The 'predictor', or independent variables were divided into two groups on the basis of their level, that is the characteristics on the level of the residents or characteristics on the level of the nursing homes table . The medFurthermore, we asked the nursing home physician which impairments or limitations had the most impact on the residents' health status. The impairments mentioned were categorised as 'impairments in mental functions' or as 'limitations in mobility and self-care'. Impairments and limitations in activity that could not be classified into one of these two categories, such as impairments in functions of the skin, genitourinary functions and limitations in communication, were treated as a reference group.Additionally, limitations in communication, limitations in behaviour, age, sex, the length of stay since admission, the reason for admission (rehabilitation versus long term care) and whether the resident was staying on a somatic or psycho-geriatric ward, were used as independent variables ,25. On tDescriptive statistics for all variables were calculated with SPSS 11.5. Multilevel logistic regression analysis, carried out by MLwiN 1.1, was used to define the amount of variation in the provision of PT between nursing homes, and the factors that determine whether a resident receives PT or not. By means of multilevel linear regression, the amount of variation in the mean volume of PT between nursing homes and the factors that act on this volume were analysed. This latter analysis was carried out for the subgroup of residents that actually received PT. Multilevel analysis was used because the data had an intrinsic hierarchical nature. The sample of residents, level 1, is nested in the sample of nursing homes, level 2. The data, therefore, were not from independent observations, violating a major assumption of traditional regression analysis. In multilevel analysis, multiple levels are taken into account and in linear multilevel regression the proportion of variance at each level can be calculated. The order of adding predictor variables was determined by their level. The analysis was carried out in four steps:1. Firstly, an intercept model was made, which is a model without any independent variables. This model estimates, in case of logistic regression, the mean percentage of the nursing home residents that receive allied health care. In case of a linear regression, the intercept model estimates the mean volume of therapy that is provided to the recipients. This latter model also establishes the contribution of both levels to the variation in the volume of treatment.2. In the next step, all independent variables at the level of the residents were added. The contribution of each factor is expressed by the regression coefficient (β) and the standard error (SE).3. In the third step, all independent variables on the level of the nursing home were added.4. In the last step, only the factors that significantly contribute to the utilisation of PT are left in the model, that is the factors for which the quotient of β and SE is larger than |1.96|, the level of significance being 0.05.For categorical independent variables indicator coding was used with the last category in each group treated as the reference group. The continuous independent variable residents' age was centred around the median age.The mean age of the 600 residents included was 81,53 years (sd: 8.24). There were 391 women and 209 men. Most residents were admitted for long term institutional care (82.3%), and less than one fifth for rehabilitation (Table Four hundred and four residents received PT (67.3%), on average 55 minutes a week. The medical diagnoses most frequently found were dementia 43.3%) and stroke (24.2%). Forty-four percent of the residents (44.3%) suffered most from limitations in mobility and self-care, while for 35.7% of the residents impairments in mental functions had the largest impact on their health status . The mean number of additional impairments and limitations in activities was 3.45 (sd: 1.75), while 75% of the residents suffered from more than one impairment or limitation. Almost half of the residents had limitations in behaviour (49%) or communication (45%). Of the 15 nursing homes, nine were located in an urban region and six in a more rural environment. Two nursing homes only provided care to residents with somatic problems, while 13 homes also had psycho-geriatric wards. Only two nursing homes did not have a stroke unit.On average 69% of the residents of the participating 15 nursing homes received PT . The percentage of residents receiving PT differed significantly across the 15 nursing homes, and this variance between nursing homes did not decrease after adding the independent variables on the residents' level. Adding the characteristics of the nursing homes itself, however, reduced the variance between nursing homes by 85%, the remaining difference being no longer significant. Apparently, the characteristics of the 15 nursing homes themselves contribute the most to the significant difference in the percentage of PT recipients among nursing homes.The factor most associated with the question whether a resident received PT in a nursing home was which ward they were on. Residents admitted for rehabilitation and those residents staying on a somatic ward received PT more often. Men were more often referred to PT than women. Furthermore, the amount of available PT personnel was associated with the chance of receiving treatment. The more FTE present in the nursing home, the more likely a resident was to receive PT. Co-morbidity enlarged the chance of receiving PT Table . FinallyThe residents referred to PT (N = 404), received 55 minutes of care a week on average (sd: 41). The amount of PT provided to a resident was not significantly different across nursing homes, although differences were considerable . Most variance in the volume of treatment was located among residents (93%), while 7% of the variance was found among nursing homes.Just as with the provision of PT, the resident's ward is the factor most associated with the amount of treatment given. Residents admitted for rehabilitation received more PT per week compared to residents admitted for long-term institutional care. Residents recovering after a total hip replacement received more PT per week compared to residents with other diagnoses Table . FurtherThis study is the first one to examine how many Dutch nursing home residents receive PT and the extent to which the provision of PT varies across nursing homes. For this purpose, a random sample of nursing homes was taken, stratified for type and size of the nursing home, leading to representative representation of the population of Dutch nursing homes table . The numOn average 69% of the nursing home residents received PT. In comparison with the international literature this is a rather high percentage of the nursing home population. This may, however, be partly due to different definitions of receiving treatment. In this study, all physiotherapeutic care as registered in the medical file, was designated as PT, without time restrictions for the amount of treatment per week. Adopting a minimum restriction of 30 minutes PT a week, as was used by Berg et al (1997) in some of the investigated countries, would result in 50% residents being treated . FurtherIn this study the medical diagnosis was not associated with whether or not a resident receives PT. This finding is, however, somewhat biased by the fact that most residents with dementia stay on a psycho-geriatric ward, leading to a high correlation between the factor 'dementia' and the ward a residents is staying on. So when the factor 'somatic ward' is left out of the analysis, residents with dementia are less likely to receive PT, leaving all other predictor variables unchanged. Furthermore, residents admitted for rehabilitation and residents living on a somatic ward were more likely to receive PT than residents admitted for long term care or residents living at a psycho-geriatric ward, irrespective of their medical diagnoses. While it may be obvious that residents admitted for rehabilitation need physiotherapeutic care, it carries the risk that residents admitted for long-term institutional care do not receive the treatment they might need, because therapists assume that treatment is less effective for this group and residents admitted for rehabilitation have precedence in cases of limited availability of PT. However, the studies by Arlings and by Przybylski 1996) revealed that increasing the amount of PT and OT did have a positive effect on the functional status of long-term care residents . The pat revealedSo despite the fact that there is evidence that PT can be effective for long-term residents and for residents with low cognitive functioning, these residents receive less treatment. This suggests an under-use of PT for long-term residents and residents with low cognitive functioning. It is, therefore, recommended that physiotherapists working in nursing homes carefully reconsider which residents may benefit from treatment. This may need a shift in the focus of the physiotherapist, and other rehabilitation disciplines, from 'recovery and discharge' to 'quality of life and well-being' of residents. The relevance of such a change of philosophy is illustrated by the fact that a relatively large proportion of elderly residents who have been admitted for rehabilitation, will not be discharged home again and eventually remains at the nursing home receiving long-term care.In our study, men were more likely to receive PT than women, irrespective of their medical condition. The reasons for this can only be hypothesised. Since most women survive their husbands, men may be more motivated for treatment and have more opportunities to return home, if their wife is capable of providing the necessary care and social support . HoweverThis study has some limitations. It was not possible to take the severity of the residents' medical problems and impairments into account, due to the method of gathering data. Data were collected through interviews with the nursing home physician and the physiotherapist, and were based on the residents' medical file. The severity of the patient's condition or their functional status were not asked, since these aspects are not always registered in a standardised manner, making a comparison between different nursing homes difficult. There are, however, no reasons to expect systematic differences in the severity of the medical problems between the residents populations of the 15 nursing homes. Another limitation is that the accuracy of assessing the medical diagnosis and the presence of impairments in mental functions and limitations in mobility and self-care, relies on the nursing home physician reports. Since diagnostic procedures used for establishing the residents' health problems were not standardised, they may differ between nursing homes.The chance of nursing home residents receiving PT differs significantly among nursing homes. This difference between nursing homes is highly associated with the difference in supply of physiotherapists. Also within the nursing home the opportunities for receiving PT are unequal, while it is most likely provided to male residents with good cognitive functioning who have recently been admitted for rehabilitation. Without wanting to deny the great need of most residents admitted for rehabilitation for physiotherapeutic treatment, this suggests a potential under-use of PT for specifically female long-term residents with cognitive problems. Since there is evidence from the literature that residents with cognitive problems and residents receiving long-term care may also benefit from PT, these groups deserve attention from PT services. Treatment goals for these residents may be formulated better in terms of quality of life and well-being instead of recovery and discharge. Furthermore, more insight is needed into the residents' needs and the efficiency of the care provided, in order to establish the optimum amount of PT for nursing home residents. This applies to both candidates for rehabilitation and for residents admitted for long-term care.The author(s) declare that they have no competing interests.CL, MEdB, CHMvdE, MWR and JD contributed to the design of this study and the main ideas of this paper. MEdB organised and performed the data-collection. CL performed the statistical analysis and drafted the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Previous studies suggested increased cancer incidence and mortality in workers exposed to solvents and other chemicals in computer manufacturing jobs. Most previous studies were of small cohorts and findings were inconsistent. A lawsuit involving a large U.S. company produced a data file for analysis. This study sought to elucidate patterns of mortality in workers who were engaged manufacturing computers and related electronic components in the largest database available to date.A proportional mortality and proportional cancer mortality analysis of deaths in eligible workers between 1969 and 2001 was carried out, with U.S. population mortality data as the standard for comparison. Mortality and work history data was from corporate mortality and work history files produced during litigation and standard U.S. and state mortality files. The study base comprised 31,941 decedents who died between 1969 and 2001, who had worked for at least five years and whose death information was collected in the corporate mortality file. Proportional mortality ratios (PMRs) and Proportional Cancer Mortality Ratios (PCMRs) and their 95% confidence intervals were computed for 66 causes of death in males and females.PMRs for all cancers combined were elevated in males  and females ; several specific cancers and other causes of death were also significantly elevated in both males and females. There were reduced deaths due to non-malignant respiratory disease in males and females and heart disease in females; several specific cancers and other causes of death were significantly reduced in both males and females. Proportional cancer mortality ratios (PCMRs) for brain and central nervous system cancer were elevated , kidney cancer , melanoma of skin  and pancreatic cancer  were significantly elevated in male manufacturing workers. Kidney cancer  and cancer of all lymphatic and hematopoietic tissue  were significantly elevated in female manufacturing workers.Mortality was elevated due to specific cancers and among workers more likely to be exposed to solvents and other chemical exposures in manufacturing operations. Due to lack of individual exposure information, no conclusions are made about associations with any particular agent. In 2002, plaintiffs' attorneys in a lawsuit obtained computerized files of deaths and work histories for US employees of a large company which manufactured electronic office equipment; mainframe and personal computers; computer parts, components, and accessories; and software products. The files contained data on employees who had qualified for pension and death benefits and died in the US between the years 1969 and 2001. A unique identifier was included in both files so that death and work history data for individual decedents could be linked.Few studies of workers in computer manufacturing and semiconductor fabrication are available in the published literature. A study of workers at a Scottish semiconductor plant was conducted by the government occupational health authority in 2001 . Two stuThere are a variety of exposures to chemicals, metals , and electromagnetic fields  in computer and semiconductor manufacturing . There hBased upon the available literature, workers exposed to the chemicals and processes in computer and semiconductor manufacturing would be expected to have elevated mortality from non-Hodgkin's lymphoma ,11, braiWe evaluated these expectations and examined the mortality among US employees of a large computer manufacturing company by calculating overall and sex-, age-, and time-specific proportional mortality ratios (PMRs) and proportional cancer mortality ratios (PCMRs). We performed analyses for all eligible decedents and the subgroup of decedents who were ever employed 30 days or more in manufacturing.The context of this analysis is unusual because the data were obtained as part of litigation and the company's attorneys resisted making the files available. A court order forced the production of the files, and minimal documentation was provided about the content and coding of the files. Due to the context in which we received these data, we were not able to obtain additional documentation, access the original or complementary data, or contact company personnel knowledgeable about the data. Since we could not resolve problems in the data, we spent considerable time developing logical rules for handling incomplete and apparently erroneous information. These constraints limited the data available for subgroup analyses.At the time it was provided to plaintiffs' attorneys, the IBM Corporate Mortality File (CMF) maintained by the company had 33,730 records. These records were for decedents who had been employed for five years or more, who were actively employed or receiving retirement or disability benefits at time of death, and whose families had filed a claim for death benefits. CMF data required for analysis were the identifier, sex, birth date, death date, and underlying cause of death.We reviewed the numbers of deaths by year of death. Based on the apparent degree of completeness of the CMF by year of death, we defined the study period as 1969–2001. We excluded duplicate records and records for which the sex, birth date, and/or death date remained unknown or discrepant. Of the 33,730 records in the CMF, 1,456 records were excluded for these reasons. Processing of the work history data (see below) identified 20 additional CMF records with discrepant birth and/or death dates and these also were excluded. We then excluded 313 CMF records for which the year of death was before 1969 or after 2001.The final number of CMF records used for analysis was 31,941 and comprised 27,272 males and 4,669 females.The work history data set had a total of 586,258 records representing 18,928 decedents. Work history data required for analysis were the identifier, effective date, position code, location code, and text notes indicating a key employment event such as leave of absence, return from leave of absence, separation, retirement, or death.We added the birth and death dates from the CMF to the work history data set. We then identified irrelevant, missing, invalid, and inconsistent work history data to the extent feasible.We deleted all work history records for decedents that were excluded from the CMF (see above). Likewise, we deleted all work history records with irrelevant, missing, invalid or inconsistent data. Upon completion of these steps, the work history data set included a total of 375,563 records representing 10,219 decedents. There did not appear to be any systematic pattern in the missing data that would produce bias in the analysis of this subset of decedents.The underlying causes of death were coded to the Ninth Revision of the International Classification of Diseases (ICD 9).We selected the US as the most appropriate comparison population for the overall CMF analyses and we used it as the comparison population for the exposure subgroup analyses for consistency. We obtained cause-, sex-, age-, and time-specific mortality data, adjusted to ICD 9, for the US comparison population from the Mortality and Population Data System (MPDS) at the University of Pittsburgh . We usedWe analyzed the data with the Occupational Cohort Mortality Analysis Program  software package from the University of Pittsburgh . In addiStandardized proportional mortality ratios (PMRs) were calculated from the ratio of the observed number of deaths due to a particular cause among the workers to the expected number of deaths based on the age-, sex-, time-, and cause-specific proportional mortalities of the US population. The expected number of deaths was computed by multiplying the proportional mortalities of the US population by the total number of deaths for the workers. The PMRs were standardized on age, sex, and time. Standardized proportional cancer mortality ratios (PCMRs) also were calculated. For the PCMRs, site-specific cancer proportional mortality was computed as a proportion of all cancer mortality. The underlying cause of death was used for calculation of the PMRs and PCMRs. Workers with an unknown cause of death were included in the "all causes of death" and "unknown causes" categories. The associated 95% Poisson confidence intervals (95% CIs) for the PMRs and PCMRs were calculated as well.We defined the following exposure subgroups for both men and women:• ever worked in manufacturing for 30 days or more;• ever worked at a selected California, Minnesota, New York and Vermont location for 30 days or more.The latter four state sub-groups were defined for analyses of specific plants of interest in the litigation (data not shown).The number of decedents and the percent of decedents with available and usable work history information are shown in Table The all cause average age at death was 64.5 years for male decedents and 63.4 for female decedents. The all cancer average age at death was 63.9 years for male decedents and 59.5 for female decedents.Cause-specific PMRs and 95% CIs for male decedents in the CMF were calculated and the results for major causes of death are summarized in Table In addition, statistically significant excesses were observed for benign neoplasms, nervous system diseases, ischemic heart disease, and all other causes of death, although the excess for ischemic heart disease was quite small. Statistically increased PMRs for multiple sclerosis, Parkinson's disease, and amyotrophic lateral sclerosis contributed to the excess for diseases of the nervous system. There also were deficits of several causes of death as estimated by the PMR method.In Table The cause-specific PMRs and 95% CIs for the 4,669 female decedents in the CMF also were calculated and the results for major causes of death are summarized in Table In Table In Table In Table In Fig. We found an overall excess mortality for all cancer in males and females in the entire corporate mortality data. The PMRs for all sites were significantly elevated when comparing the computer company workers to the US population experience during the time period 1969–2001. In addition, there was excess mortality in both males and females due to several types of cancer.Because of the potential for lowered mortality due to major causes such as ischemic heart disease or respiratory disease, we calculated PCMRs, which focused specifically on cancer mortality. The PCMR is less susceptible to distortion by the "healthy worker effect," and focuses on the health outcome of most interest in these analyses. The results of the PCMR analyses in males were for the most part quite similar to the PMR results for the same cancers. For cancer of the testis, however, the PCMR was 91 95% CI 63,130) as compared to a PMR of 146. In females, the PCMR for breast cancer was significantly elevated, while the PCMRs for several other cancers were not, compared to their respective PMRs. A recent SMR analysis of workers at three of the plants over an earlier time period in this large company [3,130 as Cancer mortality patterns in the general population over the time period 1969–2001 changed somewhat. For example, lung cancer mortality increased dramatically in US females during this time period while cervical cancer mortality decreased. In males, lung cancer mortality increased in the early part of the time period, but began to decline in the 1990s. Accordingly, we examined the workers' mortality experience in five-year time intervals, and we looked at patterns by five-year age groups during these intervals. The excesses of some cancers were greater in the younger age groups and in the 1970s and 1980s rather than the latter years of the study period. These patterns are consistent with a work-related cancer mortality effect that has diminished in recent years.Additional analyses of specific subgroups of decedents were carried out, primarily because of concerns that toxic exposures used in manufacturing, such as solvents, metals, electromagnetic radiation, and the components of photoresist might be associated with increased cancer mortality. For example, numerous halogenated solvents are used in computer manufacturing ,2,10 andThe overall PMR and PCMR analyses for manufacturing workers revealed a pattern that was similar to the pattern in all workers combined. In most instances, the PMRs and PCMRs that were elevated in the total CMF were somewhat more elevated in the manufacturing workers. The decreased mortality due to respiratory system cancer in males in the CMF was similar in the manufacturing subgroup. However, all the manufacturing subgroup analyses were based on fewer deaths and were therefore statistically less stable. In males employed in manufacturing, there was consistent increased mortality from cancer of digestive organs, kidney cancer, melanoma of skin and cancer of the brain and central nervous system. The PCMRs for several of these cancers were significantly elevated and there was also excess mortality due to non-Hodgkin's lymphoma within the broader category of lymphatic and hematopoietic malignancies. Among female manufacturing workers, we found the breast cancer PMR to be slightly elevated, while the PMRs for kidney cancer and lymphatic and hematopoietic tissue malignancies were significantly elevated but based on few deaths. The female manufacturing workers PCMRs were based on very few deaths and followed a similar pattern.Mortality analyses such as the one we conducted are limited by the availability of information on death certificates, and typically this does not permit controlling for confounding factors such as prior occupational exposures, non-occupational exposures, or cigarette smoking . SimilarAs noted above, this study was conducted in the context of litigation, so we were unable to communicate with the record providers to clarify inconsistencies or fill gaps in the information provided. Here again, we do not consider this to be a significant source of bias, but it limits the power of the sub-group analyses. If additional work is carried out on this database, it would be desirable to do this in an atmosphere of open communication.PMR, Proportional mortality ratioPCMR, Proportional cancer mortality ratioCI, Confidence IntervalIBM, International Business MachinesCMF, Corporate Mortality Fileth revisionICD 9, International Classification of Diseases, 9OCMAP, Occupational Cohort Mortality Analysis ProgramSMR, Standardized Mortality RatioThe author was previously paid a consultancy by the plaintiffs' law firm; the law firm did not design or conduct the study, nor review or approve the manuscript. The author received no remuneration for the preparation of this manuscript.RC was responsible for the overall conception and presentation of the work.