Patients and methods: An observational study was conducted at four different intensive care units of an academic medical institution. Demographic characteristics, disease-management casemix information, cost and outcome of the high costing decile, and the rest of the cases were compared.
Results: A total of 3,220 discharges were included in the study. The high-cost group contributed 35.4% of the ICU stays and 38.8% of the total ICU expenditure. Diseases of the central nervous system had higher odds to be in the top decile of costly patients whereas the cardiovascular system was more likely to be in the non-high cost category. The high-cost patients were more likely to have death as an outcome (19.2% vs 9.3%; p<0.001). The most common conditions that were in the high-cost groups were craniotomy, other ear, nose, mouth, and throat operations, simple respiratory system operations, complex intestinal operations, and septicemia. These five diagnostic groups made up 43% of the high-cost decile.
Conclusion: High-cost patients utilized almost 40% of the ICU cost although they were only 10% of the ICU patients. The chances of admission to the ICU increased with older age and severity level of the disease. Central nervous system diseases were the major problem of patients aged 46-69 years old. In addition to cost reduction strategies at the treatment level, detailed analysis of these cases was needed to explore and identify pre-event stage prevention strategies.
METHODS: All patients admitted to UKMMC in 2011 were recruited in this study. Combination of Step-down and Bottom-up costing methodology has been used in this study. The drug and supplies cost; the cost of staff; the overhead cost; and the equipment cost make up the four components of pharmacy. Direct costing approach has been employed to calculate Drugs and supplies cost from electronic-prescription system; and the inpatient pharmacy staff cost, while the overhead cost and the pharmacy equipments cost have been calculated indirectly from MY-DRG data base. The total pharmacy cost was obtained by summing the four pharmacy components' cost per each MY-DRG. The Pharmacy service weight of a MY-DRG was estimated by dividing the average pharmacy cost of the investigated MY-DRG on the average of a specified MY-DRG (which usually the average pharmacy cost of all MY-DRGs).
RESULTS: Drugs and supplies were the main component (86.0%) of pharmacy cost compared o overhead cost centers (7.3%), staff cost (6.5%) and pharmacy equipments (0.2%) respectively. Out of 789 inpatient MY-DRGs case-mix groups, 450 (57.0%) groups were utilized by the UKMMC. Pharmacy service weight has been calculated for each of these 450 MY-DRGs groups. MY-DRG case-mix group of Lymphoma & Chronic Leukemia group with severity level three (C-4-11-III) has the highest pharmacy service weight of 11.8 equivalents to average pharmacy cost of RM 5383.90. While the MY-DRG case-mix group for Circumcision with severity level one (V-1-15-I) has the lowest pharmacy service weight of 0.04 equivalents to average pharmacy cost of RM 17.83.
CONCLUSION: A mixed approach which is based partly on top-down and partly on bottom up costing methodology has been recruited to develop MY-DRG case-mix pharmacy service weight for 450 groups utilized by the UKMMC in 2011.
METHODS: Data were derived from 360 inpatient medical records from six types C public and private hospitals in an Indonesian rural province. These data were accumulated from inpatient medical records from four major disciplines: medicine, surgery, obstetrics and gynecology, and pediatrics. The dependent variable was provider moral hazards, which included indicators of up-coding, readmission, and unnecessary admission. The independent variables are Physicians' characteristics (age, gender, and specialization), coders' characteristics (age, gender, education level, number of training, and length of service), and patients' characteristics (age, birth weight, length of stay, the discharge status, and the severity of patient's illness). We use logistic regression to investigate the determinants of moral hazard.
RESULTS: We found that the incidences of possible unnecessary admissions, up-coding, and readmissions were 17.8%, 11.9%, and 2.8%, respectively. Senior physicians, medical specialists, coders with shorter lengths of service, and patients with longer lengths of stay had a significant relationship with the incidence of moral hazard.
CONCLUSION: Unnecessary admission is the most common form of a provider's moral hazard. The characteristics of physicians and coders significantly contribute to the incidence of moral hazard. Hospitals should implement reward and punishment systems for doctors and coders in order to control moral hazards among the providers.
METHODS: The HomeSat subscale of the Dutch SASC-19 questionnaire (11 items) underwent back-to-back translation to produce a Malay language version. Content validation was done by Family Medicine Specialists involved in community post-stroke care. Community social support services in the original questionnaire were substituted with equivalent local services to ensure contextual relevance. Internal consistency reliability was determined using Cronbach alpha. Exploratory factor analysis was done to validate the factor structure of the Malay version of the questionnaire (SASC10-My™). The SASC10-My™ was then tested on 175 post-stroke patients who were recruited at ten public primary care healthcentres across Peninsular Malaysia, in a trial-within a trial study.
RESULTS: One item from the original Dutch SASC19 (HomeSat) was dropped. Internal consistency for remaining 10 items was high (Cronbach alpha 0.830). Exploratory factor analysis showed the SASC10-My™ had 2 factors: discharge transition and social support services after discharge. The mean total score for SASC10-My™ was 10.74 (SD 7.33). Overall, only 18.2% were satisfied with outpatient stroke care services (SASC10-My™ score ≥ 20). Detailed analysis revealed only 10.9% of respondents were satisfied with discharge transition services, while only 40.9% were satisfied with support services after discharge.
CONCLUSIONS: The SASC10-My™ questionnaire is a reliable and valid tool to measure caregiver or patient satisfaction with outpatient stroke care services in the Malaysian healthcare setting. Studies linking discharge protocol patterns and satisfaction with outpatient stroke care services should be conducted to improve care delivery and longer-term outcomes.
TRIAL REGISTRATION: No.: ACTRN12616001322426 (Registration Date: 21st September 2016.
METHODS: We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs).
FINDINGS: In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505).
INTERPRETATION: Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.
METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced.
RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes.
CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.
METHODS: Injury mortality was estimated using the GBD mortality database, corrections for garbage coding and CODEm-the cause of death ensemble modelling tool. Morbidity estimation was based on surveys and inpatient and outpatient data sets for 30 cause-of-injury with 47 nature-of-injury categories each. The Socio-demographic Index (SDI) is a composite indicator that includes lagged income per capita, average educational attainment over age 15 years and total fertility rate.
RESULTS: For many causes of injury, age-standardised DALY rates declined with increasing SDI, although road injury, interpersonal violence and self-harm did not follow this pattern. Particularly for self-harm opposing patterns were observed in regions with similar SDI levels. For road injuries, this effect was less pronounced.
CONCLUSIONS: The overall global pattern is that of declining injury burden with increasing SDI. However, not all injuries follow this pattern, which suggests multiple underlying mechanisms influencing injury DALYs. There is a need for a detailed understanding of these patterns to help to inform national and global efforts to address injury-related health outcomes across the development spectrum.
METHODS: We used results from the Global Burden of Disease (GBD) 2017 study to report incidence, prevalence, years lived with disability, deaths, years of life lost and disability-adjusted life years for all locations in the GBD 2017 hierarchy from 1990 to 2017 for road injuries. Second, we measured mortality-to-incidence ratios by location. Third, we assessed the distribution of the natures of injury (eg, traumatic brain injury) that result from each road injury.
RESULTS: Globally, 1 243 068 (95% uncertainty interval 1 191 889 to 1 276 940) people died from road injuries in 2017 out of 54 192 330 (47 381 583 to 61 645 891) new cases of road injuries. Age-standardised incidence rates of road injuries increased between 1990 and 2017, while mortality rates decreased. Regionally, age-standardised mortality rates decreased in all but two regions, South Asia and Southern Latin America, where rates did not change significantly. Nine of 21 GBD regions experienced significant increases in age-standardised incidence rates, while 10 experienced significant decreases and two experienced no significant change.
CONCLUSIONS: While road injury mortality has improved in recent decades, there are worsening rates of incidence and significant geographical heterogeneity. These findings indicate that more research is needed to better understand how road injuries can be prevented.