Dataset Information

What makes the hospitalisation system more efficient? An application of the decomposition method to Hong Kong morbidity data.

ABSTRACT:

Objective

To examine the efficiency of the Hong Kong hospitalisation system based on hospitalisation days.

Design

Retrospective study.

Setting

Hospitalisation data (2000-2010) for all government-funded hospitals in Hong Kong.

Population

Hospitalisation data for the entire Hong Kong population (7.0 million in 2011).

Methods

A decomposition method was used to determine the effects on total hospitalisation days during the period 2000-2010 of the following three factors: (i) hospitalisation rate per person; (ii) the number of visits per patient; and (iii) the mean duration of stay per visit.

Main outcome measures

The decomposition method provides empirical measures of how the three factors contributed to the change in total hospitalisation days during the period 2000-2010 and identifies the most effective way to contain increases in hospitalisation days.

Results

The results of decomposition analysis show that the decrease in mean duration of stay per visit (reducing from 6.83 to 4.58 days) is the most important factor in the reduction in the total number of hospitalisation days, despite increases in total population size (from 6.7 to 7.0 million), the number of individual hospital admissions (from 583 000 to 664 000) and the number of episodes (from 1.2 to 1.4 million) from 2000 to 2010. Hospitalisation days per person decreased from 1.18 in 2000 to 0.93 in 2010. The decline in the mean duration of stay per visit contributed 200.6% to this reduction but was offset by -51.1% due to a slight growth in the number of visits per patient and by -49.4% as a result of changed hospitalisation rates per person.

Conclusions

Better management of the duration of stay of per visit without compromising patient satisfaction levels or the quality of service is the most important factor for controlling increases in health expenditure in Hong Kong.

SUBMITTER: Yip PS

PROVIDER: S-EPMC3948574 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

What makes the hospitalisation system more efficient? An application of the decomposition method to Hong Kong morbidity data.

Yip Paul S F PS Lee Carmen K M CK Chow Chun-Bong CB Lo William T L WT

BMJ open 20140306 3

<h4>Objective</h4>To examine the efficiency of the Hong Kong hospitalisation system based on hospitalisation days.<h4>Design</h4>Retrospective study.<h4>Setting</h4>Hospitalisation data (2000-2010) for all government-funded hospitals in Hong Kong.<h4>Population</h4>Hospitalisation data for the entire Hong Kong population (7.0 million in 2011).<h4>Methods</h4>A decomposition method was used to determine the effects on total hospitalisation days during the period 2000-2010 of the following three f ...[more]

PMID: 24604479

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Project description:Ageing has become a serious challenge in Hong Kong and globally. It has serious implications for health expenditure, which accounts for nearly 20% of overall government expenditure. Here we assess the contribution of ageing and related factors to hospitalisation days in Hong Kong. We used hospital discharge data from all publicly funded hospitals in Hong Kong between 2001 and 2012.A decomposition method was used to examine the factors that account for the change of total hospitalisation days during the two periods, 2001-2004 and 2004-2012. The five factors include two demographic factors - population size and age-gender composition - and three service components - hospital discharge rate, number of discharge episodes per patient, and average length of stay (LOS) - which are all measured at age-gender group level. In order to assess the health cost burden in the future, we also project the total hospitalisation days up to 2041, for a range of scenarios.During the decreasing period of hospitalisation days (2001-2004), the reduction of LOS contributed to about 60% of the reduction. For the period of increase (2004-2012), ageing is associated with an increase in total hospitalisation days of 1.03 million, followed by an increase in hospital discharge rates (0.67 million), an increase in the number of discharge episodes per patient (0.62 million), and population growth (0.43 million). The reduction of LOS has greatly offset these increases (-2.19 million days), and has become one of the most significant factors in containing the increasing number of hospitalisation days. Projected increases in total hospitalisation days under different scenarios have highlighted that the contribution of ageing will become even more prominent after 2022.Hong Kong is facing increasing healthcare burden caused by the rapid increase in demand for inpatient services due to ageing. Better management of inpatient services with the aim of increasing efficiency and reducing LOS, avoidable hospitalisation and readmission, without compromising patient satisfaction and quality of service, are crucial for containing the rapid and enormous increases in total hospitalisation days for Hong Kong. The results would be relevant to many rapidly ageing societies in this region.

Project description:BackgroundUp-to-date and accurate information about the health problems encountered by primary care doctors is essential to understanding the morbidity pattern of the community to better inform health care policy and practice. Morbidity surveys of doctors allow documentation of actual consultations, reflecting the patient's reason for seeking care as well as the doctor's diagnostic interpretation of the illness and management approach. Such surveys are particularly critical in the absence of a centralized primary care electronic medical record database.ObjectiveWith the changing sociodemographic profile of the population and implementation of health care initiatives in the past 10 years, the aim of this study is to determine the morbidity and management patterns in Hong Kong primary care during a pandemic and compare the results with the last survey conducted in 2007-2008.MethodsThis will be a prospective, practice-based survey of Hong Kong primary care doctors. Participants will be recruited by convenience and targeted sampling from both public and private sectors. Participating doctors will record the health problems and corresponding management activities for consecutive patient encounters during one designated week in each season of the year. Coding of health problems will follow the International Classification of Primary Care, Second Edition. Descriptive statistics will be used to calculate the prevalence of health problems and diseases as well as the rates of management activities (referral, investigation, prescription, preventive care). Nonlinear mixed effects models will assess the differences between the private and public sectors as well as factors associated with morbidity and management patterns in primary care.ResultsThe data collection will last from March 1, 2021, to August 31, 2022. As of April 2022, 176 doctor-weeks of data have been collected.ConclusionsThe results will provide information about the health of the community and inform the planning and allocation of health care resources.Trial registrationClinicalTrials.gov NCT04736992; https://clinicaltrials.gov/ct2/show/NCT04736992.International registered report identifier (irrid)DERR1-10.2196/37334.

Project description:BackgroundAdolescent travel mode choices to/from school vary in their physical activity (PA) component and environmental sustainability. Research has typically focussed on correlates of active travel, the most healthy and sustainable mode, in comparison to other modes combined. Under the premise that a small shift from less to more healthy/sustainable modes may be a more feasible than a shift to 'pure' active travel (e.g., walking), we examined potential correlates of the odds of undertaking more vs. less healthy/sustainable modes.MethodsHong Kong adolescents attending secondary school and a parent/caregiver (n?=?1299 dyads) participated in this cross-sectional study. Latent profile analyses identified groups of adolescents with different transport mode profiles to/from school. Profiles were ranked based on relative PA/sustainability outcomes. Multilevel logistic regression identified environmental, social and psychological factors associated with more vs. less PA/sustainable transport mode profiles to/from school.ResultsMost frequent transport modes were walking and public transport. Latent profile analysis resulted in a 7-profile model (walk (n?=?430); walk & public transport (n?=?93); public transport (n?=?486); bicycle, car & taxi (n?=?60); school bus to & public transport from school (n?=?54); school bus (n?=?106); car to & car/public transport from school (n?=?70)). All profile comparisons were associated with at least one environmental variable. School proximity, access to services and parent transport-related PA were generally associated with higher odds of healthier-more sustainable transport modes. Adolescent-perceived distance and effort barriers to walking and cycling were generally associated with lower odds of more healthy/sustainable modes.DiscussionMost adolescents engaged in relatively healthy/sustainable travel modes to/from school. Public transport to walking and school bus to public transport mode shifts are likely to have the biggest impact towards more healthy/sustainable modes. Encouraging parent-related transport PA may positively influence adolescent mode choice. Relatively dense, destination-rich neighbourhoods may encourage more healthy/sustainable transport modes to/from school by providing easy access to schools and services.ConclusionGovernment policy encouraging enrolment in the closest local school and private school encouragement of public transport rather than school buses may have the greatest impact on shifts to more healthy/sustainable transport modes to/from school in Hong Kong adolescents.

Dataset Information

What makes the hospitalisation system more efficient? An application of the decomposition method to Hong Kong morbidity data.

Objective

Design

Setting

Population

Methods

Main outcome measures

Results

Conclusions

Publications

What makes the hospitalisation system more efficient? An application of the decomposition method to Hong Kong morbidity data.

Similar Datasets

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