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PubMed2000

Unrepresentative, invalid and misleading: are waiting times for elective admission wrongly calculated?

THESIS: The UK Government Statistical Service reports the percentage of elective 'admissions' that took place in England within 3 months of a patient being added to NHS waiting lists. This percentage is calculated from cross-sectional data using the total number of elective episodes within a specified calendar period as denominator and the number of these enrolled on the waiting list less than 3 months previously as numerator. This approach assumes that NHS waiting lists are closed and stationary populations, and has been widely used by government and non-government researchers in the UK and elsewhere. ANTITHESIS: Little attention has been given to the bias introduced when waiting lists are neither stationary nor closed. This paper identifies four groups of patients which are excluded from the denominator used by the Government Statistical Service and criticises the established method of ignoring left and right censored observations. SYNTHESIS: We describe two alternative formulae that would give the same results as the Government Statistical Service method if waiting lists were closed and stationary, but that also give unbiased results when waiting lists are open and non-stationary. They require a limited amount of additional cross-sectional data to produce upper and lower estimates of the cumulative likelihood of admission among those listed. We recommend the production of unbiased estimates by applying period life-table techniques to a complete and consistent set of 'times since enrolment'.

Cohort Studies↗
PubMed1990

Harnessing information technology to revolutionise teaching, research and consultative service in health statistics.

Computers have traditionally been associated with health statistics because of the need to manage, process and analyse data. Over the last two years, the Division of Biostatistics and Health Informatics of the Department of Community, Occupational and Family Medicine at the National University of Singapore has been actively looking into ways in which informatics and telematics could be usefully applied to teaching and research in health statistics and in the provision of a statistical information service. This paper describes three projects, originating from the National University of Singapore, which provide an effective demonstration of the application of modern technology to an age-old discipline.

Computer-Assisted Instruction↗
PubMed1994

Seasonal variation of neonatal and infant deaths by cause in Greece.

The seasonal variation of neonatal and infant deaths in Greece was analyzed for nine consecutive years (1979-1987) by cause of death, age of death and urbanization of permanent residence. Data were supplied by the National Statistical Service of Greece. Statistical analysis was done using the Edward's method. The seasonal patterns of the number of deaths and death rates were similar. Neonatal deaths in total did not show significant seasonality but postneonatal deaths showed seasonal variation with a peak in the winter, more evident in rural areas. Neonatal deaths from prematurity showed statistically significant seasonal variation with a peak in May. Postneonatal deaths from infections and mainly those from pneumonia showed very significant seasonal variation with a peak in February that was more prominent in rural areas. Seasonal pattern with peak in late winter was also found for postneonatal deaths from injuries. The seasonal patterns for neonatal and postneonatal deaths from sudden infant death syndrome were suggestive of an increased occurrence during the winter months mainly in urban areas.

Age Factors↗
PubMed2002

The ebb and flow of the NHS waiting list: how do recruitment and admission affect event-based measures of the length of 'time-to-admission'?

The likelihood of admission is reported in England as the percentage of elective episodes occurring within a certain time, for example, within three months of the date of enrollment on the waiting list. This event-based measure is calculated from cross-sectional data: the denominator is the number of elective episodes occurring in a specified calendar period, and the numerator is the number found to have enrolled on the waiting list less than three months previously. Now the number of elective episodes occurring within three months reflects the likelihood of admission and the numbers eligible to be admitted. If there is any increase in the likelihood of admission or in the number of people exposed to that likelihood then there will be an increase in the number of elective episodes found to have enrolled on the waiting list less than three months previously. Thus the numerator used by the Government Statistical Service accurately reflects conditions during the calendar period and within the enrollment cohorts of interest. The Government Statistical Service also needs a denominator so the episodes observed 0-2, 3-5, 6-8, 9-11 etc. months after enrollment are added as an indication of the number of people that could have been admitted within three months. This denominator implies that the number of people eligible for admission from the 3-5 month waiting time category is the same as the number surviving admission from the 0-2 month waiting time category but, during the period of interest, these two groups of people belong to cohorts that were recruited to the waiting list quite independently. As a result, this denominator will be too big if the number surviving to the end of one waiting time category is bigger than the number eligible for admission from the next and it will be too small if the number surviving to the end of one waiting time category is smaller than the number eligible for admission from the next. The event-based measure assumes that the waiting list is stationary and closed and only gives unbiased estimates under these conditions. This paper describes three alternative measures which recognize that the number of people recruited or admitted may vary from one quarter to the next. It uses Department of Health data to assess the size of the error if the event-based measure is used in these circumstances.

Elective Surgical Procedures↗
PubMed2004

Death Certification and New Zealand Health Information Service (NZHIS) statistics for diabetes mellitus: an under-recognised health problem.

The main aims were to determine how much diabetes is under-recorded on death certificates, the influence of demographic variables and to what extent New Zealand Health Information Service (NZHIS) coding is able to compensate for deficiencies. A retrospective review of 600 death certificates (during 1999) from the Mortality Review Database in Christchurch Hospitals was carried out. Clinical data, laboratory results, coroner's reports, hospital clinical coding and NZHIS coding were collated and analysed. One hundred and four cases (17%) had clinically documented diabetes, of which only 47 (45%) had diabetes recorded on either the death certificate or the coroner's report. Diabetes was recognised in NZHIS coding in 45 (43%) of cases and in 94 (90%) cases in hospital clinical coding. Diabetes was more likely to be recorded on the death certificate in males and in subjects treated with insulin (P<0.05). Of the 496 subjects without documented diabetes, 159 (32%) had a highest random plasma glucose >11.1 mmol/l. Documented diabetes is thus under-reported on more than 50% of death certificates and this is not compensated by NZHIS coding. In addition, there is likely to be a significant number of cases with undiagnosed diabetes. This may adversely affect the reliability of mortality statistics.

Adolescent↗
PubMed1988

Women's health and women's work in health services: what statistics tell us.

This article draws together statistical information in several broad areas that relate to women's health, women's reproductive activities and women's occupations in Sweden. The statistical analysis reflects the major changes that have occurred in Swedish society and that have had a major impact on the health and well-being, as well as on the social participation rate, of women. Much of the data is drawn from a recent special effort at Statistic Sweden aimed at influencing the classification, collection and presentation of statistical data in all fields in such a way that family, working, education, health and other conditions of women can be more readily and equitably compared with those of men. In addition, social changes have seen the shifting of the responsibility of health care from the unpaid duties of women in the home to health care institutions, where female employees predominate. These trends are also discussed.

Adolescent↗
PubMed2006

HIV prevalence and trends from data in Zimbabwe, 1997-2004.

BACKGROUND: This paper brings together data from a variety of reports to provide a basis for assessing future steps for responding to and monitoring the HIV epidemic in Zimbabwe. METHOD: Data reported from four antenatal clinic (ANC) surveys conducted between 2000 and 2004, two small local studies in Zimbabwe conducted from 1997 through 2003, four general population surveys from 1999 through 2003, and service statistics covering 1990 through 2004 were used to describe recent trends in HIV prevalence and incidence, behaviour change, and programme provision. RESULTS: HIV prevalence among pregnant women attending ANCs declined substantially from 32.1% in 2000 to 23.9% in 2004. The local studies confirmed the decline in prevalence. However, prevalence continued to be high. Sexual behaviour data from surveys suggests a reduction in sexual experience before age 15 years among both males and females age 15-19 years, and in the proportions of males and females aged 15-29 years reporting non-regular sexual partners in the past 12 months. Reported condom use with non-regular partners has been high since 1999. Condom distribution and HIV counseling and testing increased from 2000 to 2004. DISCUSSION: On the basis of examination of data from a variety of sources, the recent decrease in HIV prevalence may be related to recent reductions in early-age sexual activity and non-regular sexual partnerships and increases in condom use. Comparison of data from sentinel surveillance systems, population based serosurveys, local studies, and service statistics provide increased confidence that a decline in HIV prevalence in Zimbabwe is actually happening in the population.

Adolescent↗