Effects of a regional-rural immersion program in Northland, New Zealand, on returning to work in that region.

INTRODUCTION: It is well established that rural workforce outcomes are more likely among medical graduates who spend time training in non-urban areas; however, fewer studies have assessed whether graduates are more likely to return to the specific area where they undertook rural training. OBJECTIVE: This study aimed to determine whether graduates who had undertaken a regional-rural immersion program in Northland, NZ, were more likely to have returned to work in Northland as of mid-2021, relative to peers who did not participate. DESIGN: This prospective cohort study used longitudinal tracking survey responses, medical school administrative data and workforce outcome information. A multinomial model, accounting for other covariates, was built to determine the association between graduates practising in Northland (population ⟨ 100 000), which encompasses both rural (population ⟨ 25 000) and regional (25 000 ⟩ population ⟨ 100 000) areas, and having participated in a Northland-based immersion program during medical school. The study population was University of Auckland domestic medical students graduating between 2009 and 2018, inclusive. Immersion program participants who responded to longitudinal career tracking surveys were included in the study sample. FINDINGS: The final sample size was 1320 students (80% of population of interest). Graduates who undertook the Northland immersion program (n = 169) were more likely than non-participants (n = 1151) to be working in Northland as of 2020-2021 (relative risk: 3.2). DISCUSSION AND CONCLUSION: Regional-rural immersion programs might preferentially build workforces in that specific region; however, further research is required to understand whether these findings are generalizable, and the main reasons for this effect.

Factors of quality of care and their association with smartphone based PHR adoption in South Korean hospitals.

BACKGROUND: Healthcare organizations have begun to adopt personal health records (PHR) systems to engage patients, but little is known about factors associated with the adoption of PHR systems at an organizational level. The objective of this study is to investigate factors associated with healthcare organizations' adoption of PHR systems in South Korea. METHODS: The units of analysis were hospitals with more than 100 beds. Study data of 313 hospitals were collected from May 1 to June 30, 2020. The PHR adoption status for each hospital was collected from PHR vendors and online searches. Adoption was then confirmed by downloading the hospital's PHR app and the PHR app was examined to ascertain its available functions. One major outcome variable was PHR adoption status at hospital level. Data were analysed by logistic regressions using SAS 9.4 version. RESULTS: Out of 313 hospitals, 103 (32.9%) hospitals adopted PHR systems. The nurse-patient ratio was significantly associated with PHR adoption (OR 0.758; 0.624 to 0.920, p = 0.005). The number of health information management staff was associated with PHR adoption (OR 1.622; 1.228 to 2.141, p = 0.001). The number of CTs was positively associated with PHR adoption (OR 5.346; 1.962 to 14.568, p = 0.001). Among the hospital characteristics, the number of beds was significantly related with PHR adoption in the model of standard of nursing care (OR 1.003; 1.001 to 1.005, p < 0.001), HIM staff (OR 1.004; 1.002 to 1.006, p < 0.001), and technological infrastructure (OR 1.050; 1.003 to 1.006, p < 0.001). CONCLUSIONS: One-third of study hospitals had adopted PHR systems. Standard of nursing care as well as information technology infrastructure in terms of human resources for health information management and advanced technologies were significantly associated with adoption of PHR systems. A favourable environment for adopting new technologies in general may be associated with the adoption and use of PHR systems.

Using national data to model the New Zealand radiation oncology workforce.

INTRODUCTION: Demand for radiation therapy is expected to increase over time. In Aotearoa/New Zealand, the radiation oncology workforce experiences high numbers of clinical hours but an intervention rate that is lower than in comparable countries, suggesting unmet treatment need. Accurate models on the supply and demand for radiation oncologists (ROs) are needed to ensure adequate staffing levels. METHODS: We developed a demand model that predicted the future number of ROs required, using national data from the Radiation Oncology Collection (ROC) and a survey of ROs. Radiation therapy intervention and retreatment rates (IR/RTRs), and benign and non-cancer conditions being treated, were derived from the ROC and applied to Census population projections. Survey data provided definitions of treatment by complexity, time spent in different activities and time available for work. Results were linked to radiation oncology workforce forecasts from a supply model developed by the Ministry of Health. RESULTS: The demand model showed that 85 ROs would be needed in 2031, if current IR/RTRs were maintained, an increase from 68 in 2021. The supply model predicted a decrease in ROs over time, leaving a significant shortfall. Model parameters could be modified to assess the impact of workforce or practice changes; more ROs would be needed if average working hours reduced or IR/RTRs increased. CONCLUSION: Workforce models based on robust data collections are an important tool for workforce planning. The RO demand model presented here combines detailed information on treatment and work activities to provide credible estimates that can be used to inform actions on training, recruitment and retention.

Socio-economic deprivation and healthcare service use of young people with type 1 and type 2 diabetes.

AIM: Lower socio-economic status (SES) is linked to greater morbidity in people with young-onset type 2 (T2D) and type 1 diabetes (T1D). We assessed healthcare utilisation from this population and the impact of SES. METHODS: Retrospective analysis of 1,350 people with T2D and 731 with T1D diagnosed between 15-30 years of age referred to secondary diabetes services in Auckland, New Zealand. Primary care visits, referral to/attendance at diabetes clinics, and hospital admissions were recorded; their relationship to a validated national index of deprivation (NZDep) was assessed. RESULTS: The proportion with primary care attendance was similar in both groups with no significant variation with NZDep. For T2D, NZDep was a predictor of delayed referral (≧1-year post-diagnosis) to diabetes services, following adjustment for age and HbA1c in the year of diagnosis (OR 1.15 for every decile increase in NZDep, 95% CI 1.07-1.24, p=0.0003). The median number of appointments offered over a 2-year period was greater for T1D (2.0 (IQR 0, 7) vs (0 (IQR 0, 2), p<0.001); non-attendance increased with NZDep for T2D (p=0.016). The proportion with hospital admissions was similar in both groups and increased with NZDep (T1D p<0.001, T2D p=0.015). CONCLUSION: SES impacts several measures of healthcare utilisation. Current healthcare models are inadequately servicing people with young-onset T2D.

A distance-based approach to rurality and remoteness in health: concept, methodology and correlates of a patient-centred health services spatial accessibility index.

AIM: To develop a distance-based index of patients' spatial accessibility to healthcare services as a quantifiable basis for analysing health services and health outcomes in urban, rural and remote locations. METHOD: A distance score was calculated based on each primary health organisation enrollee's shortest distance to the nearest primary care facility and to the nearest secondary or tertiary hospital. The distance scores were then grouped into ten distance deciles (DDs). RESULTS: When these DDs are compared with Stats NZ's urban-rural indicator, "small urban areas" fall mainly along with rural and remote areas into the two DDs (DD9 and DD10) based on the greatest distance scores. When compared with Stats NZ's urban accessibility classification, the same two DDs correspond mainly to the most rural and remote areas. In both the North and South islands, 25% or more of enrollees in DD9 and DD10 are aged 60+. Of enrollees in DD10 in the North Island, 32% are Maori and 33% live in highly deprived areas (NZDep2013 deciles 9 and 10). CONCLUSION: The results provide an initial validation of the patient-centred health services spatial accessibility index as a measure of rurality and remoteness for analysis of health service provision and health outcomes.

Socioeconomic status and risk factors for complications in young people with type 1 or type 2 diabetes: a cross-sectional study.

INTRODUCTION: Young people with type 2 diabetes (T2D) develop complications earlier than those with type 1 diabetes (T1D) of comparable duration, but it is unclear why. This apparent difference in phenotype could relate to relative inequality. RESEARCH DESIGN AND METHODS: Cross-sectional study of young people referred to secondary diabetes services in Auckland, Aotearoa-New Zealand (NZ): 731 with T1D and 1350 with T2D currently aged <40 years, and diagnosed between 15 and 30 years. Outcome measures were risk factors for complications (glycemic control, urine albumin/creatinine ratio (ACR), cardiovascular disease (CVD) risk) in relation to a validated national index of deprivation (New Zealand Deprivation Index (NZDep)). RESULTS: Young people with T2D were an average 3 years older than those with T1D but had a similar duration of diabetes. 71% of those with T2D were of Maori or Pasifika descent, compared with 24% with T1D (p<0.001). T1D cases were distributed evenly across NZDep categories. 78% of T2D cases were living in the lowest four NZDep categories (p<0.001). In both diabetes types, body mass index (BMI) increased progressively across the NZDep spectrum (p<0.002), as did mean glycated hemoglobin (HbA1c) (p<0.001), the prevalence of macroalbuminuria (p≤0.01), and CVD risk (p<0.001). Adjusting for BMI, diabetes type, and duration and age, multiple logistic regression revealed deprivation was the strongest risk factor for poorly controlled diabetes (defined as HbA1c >64 mmol/mol, >8%); OR 1.17, 95% CI 1.13 to 1.22, p<0.0001. Ordinal logistic regression showed each decile increase in NZDep increased the odds of a higher ACR by 11% (OR 1.11, 95% CI 1.06 to 1.16, p<0.001) following adjustment for BMI, blood pressure, diabetes type and duration, HbA1c, and smoking status. Multiple linear regression indicated a 4% increase in CVD risk for every decile increase in NZDep, regardless of diabetes type. CONCLUSIONS: The apparent more aggressive phenotype of young-onset T2D is at least in part explicable by relative deprivation.

The employment gap: the relationship between medical student career choices and the future needs of the New Zealand medical workforce.

AIMS: To determine the career decision intentions of graduating doctors, and the relationship between these intentions and the predicted medical workforce needs in New Zealand in 10 years' time. METHODS: A workforce forecasting model developed by the Ministry of Health (MOH) has been used to predict the proportion of doctors required in each medical specialty in 2028 in New Zealand. The future work intentions of recently graduated doctors at the Universities of Auckland and Otago were collected from the Medical Student Outcomes Data (MSOD), and compared with these predicted needs. RESULTS: Between 2013 and 2017, 2,292 doctors graduated in New Zealand, of whom 1,583 completed the MSOD preferences section (response rate 69%). Of these only 50.1% had decided on a future medical specialty. The most popular were surgical specialties (26.2%), general practice (20.7%), and internal medicine (11.0%). Compared to the MOH workforce forecast model there appears to be insufficient interest in general practice at the time of graduation. CONCLUSIONS: To shape the medical workforce to meet forecast needs, multiple stakeholders will need to collaborate, with a special focus on the early postgraduate years, as many doctors have yet to decide on specialisation.

Can administrative health utilisation data provide an accurate diabetes prevalence estimate for a geographical region?

AIM: To validate the New Zealand Ministry of Health (MoH) Virtual Diabetes Register (VDR) using longitudinal laboratory results and to develop an improved algorithm for estimating diabetes prevalence at a population level. METHODS: The assigned diabetes status of individuals based on the 2014 version of the MoH VDR is compared to the diabetes status based on the laboratory results stored in the Auckland regional laboratory result repository (TestSafe) using the New Zealand diabetes diagnostic criteria. The existing VDR algorithm is refined by reviewing the sensitivity and positive predictive value of the each of the VDR algorithm rules individually and as a combination. RESULTS: The diabetes prevalence estimate based on the original 2014 MoH VDR was 17% higher (n = 108,505) than the corresponding TestSafe prevalence estimate (n = 92,707). Compared to the diabetes prevalence based on TestSafe, the original VDR has a sensitivity of 89%, specificity of 96%, positive predictive value of 76% and negative predictive value of 98%. The modified VDR algorithm has improved the positive predictive value by 6.1% and the specificity by 1.4% with modest reductions in sensitivity of 2.2% and negative predictive value of 0.3%. At an aggregated level the overall diabetes prevalence estimated by the modified VDR is 5.7% higher than the corresponding estimate based on TestSafe. CONCLUSION: The Ministry of Health Virtual Diabetes Register algorithm has been refined to provide a more accurate diabetes prevalence estimate at a population level. The comparison highlights the potential value of a national population long term condition register constructed from both laboratory results and administrative data.

Constructing a Real-Time Prescription Drug Monitoring System.

OBJECTIVES: The objective of this investigation was to demonstrate the possibility of the construction of a real-time prescription drug monitoring system (PDMOS) using data from the nationwide Drug Utilization Review (DUR) system in Korea. METHODS: The DUR system collects information on drug prescriptions issued by healthcare practitioners and on drugs dispensed by pharmacies. PDMOS was constructed using this data. The screen of PDMOS is designed to exhibit the number of drug prescriptions, the number of prescriptions dispensed by pharmacies, and the dispensed prescription drug costs on a daily and weekly basis. Data was sourced from the DUR system between June 1, 2016 and July 18, 2016. The TOGA solution developed by the EYEQMC Co. Ltd. of Seoul, Korea was used to produce the screen shots. RESULTS: Prescription numbers by medical facilities were more numerous than the number of prescriptions dispensed by pharmacies, as expected. The number of prescriptions per day was between 2 to 3 million. The prescriptions issued by primary care clinics were most numerous, at 75% of the total number of prescriptions. Daily prescription drug costs were found to be approximately US $50 million. The prescription drug costs were highest on Mondays and were reduced towards the end of the week. Prescriptions and dispensed prescriptions numbered approximately 1,200 and 1,000 million, respectively. CONCLUSIONS: The construction of a real-time PDMOS has been successful to provide daily and weekly information. There was a lag time of only one day at the national level in terms of information extraction, and scarcely any time was required to load the data. Therefore, this study highlights the potential of constructing a PDMOS to monitor the estimate the number of prescriptions and the resulting expenditures from prescriptions.

Development of a Virtual Diabetes Register using Information Technology in New Zealand.

OBJECTIVES: The purpose of this study was to consider a Virtual Diabetes Registry System (VDR) and to investigate what it is and how it is used in New Zealand. New Zealand has specified diabetes mellitus (DM) as a national health priority. The Ministry of Health requires an accurate method for tracking the number of people with diagnosed with DM in the population. METHODS: We combined five national databases, all of which included a unique patient identifier: hospital admissions coded for DM, outpatient attendances for DM, DM retinal screening, prescriptions of specific anti-diabetic therapies, laboratory orders for HbA1c, as well as Primary Health Organisation (PHO) enrolments and national mortality. The algorithm was progressively modified to improve sensitivity and specificity, and it was validated against primary care registers. The algorithm was still being used in 2014. RESULTS: The prevalence of diagnosed diabetes in New Zealand at December 31, 2009 was 189,256 (4.4% of whole population). The VDR is now used to determine the official diagnosed diabetes prevalence in New Zealand; it is also used to determine the denominator of the health targets that the Ministry of Health should achieve for diabetes service indicators in New Zealand. CONCLUSIONS: This method appears to be superior to any other practicable national survey and to be both accurate and robust. The VDR has become an invaluable tool for monitoring prevalence and the policy making process, and for supporting clinical quality improvement.

Healthcare Utilization Monitoring System in Korea.

OBJECTIVES: It is important to monitor the healthcare utilization of patients at the national level to make evidence-based policy decisions and manage the nation's healthcare sector. The Health Insurance Review & Assessment Service (HIRA) has run a Healthcare Utilization Monitoring System (HUMS) since 2008. The objective of this paper is to introduce HIRA's HUMS. METHODS: This study described the HUMS's system structure, capacity, functionalities, and output formats run by HIRA in the Republic of Korea. Regarding output formats, this study extracted diabetes related health insurance claims through the HUMS from August 1, 2014 to May 31, 2015. RESULTS: The HUMS has kept records of health insurance claim data for 4 years. It has a 14-terabyte hardware capacity and employs several easy-to-use programs for maintenance of the system, such as MSTR, SAS, etc. Regarding functionalities, users should input diseases codes, target periods, facility types, and types of attributes, such as the number of healthcare utilizations or healthcare costs. It also has a functionality to predict healthcare utilization and costs. When this study extracted diabetes related data, it was found that the trend of healthcare costs for the treatment of diabetes and the number of patients with diabetes were increasing. CONCLUSIONS: HIRA's HUMS works well to monitor healthcare utilization of patients at the national level. The HUMS has a high-capacity hardware infrastructure and several operational programs that allows easy access to summaries as well as details to identify contributing factors for abnormality, but it has a limitation in that there is often a time lag between the provision of healthcare to patients and the filing of health claims.

Current Status and Progress of Telemedicine in Korea and Other Countries.

OBJECTIVES: The purpose of this study was to review the current telemedicine of the Korea and the telemedicine of the other countries. METHODS: This study reviewed several documents on telemedicine and summarized the documents on the initiation of the telemedicine of the Korea, the recent regulations of the government, the analytical research results, and the telemedicine of foreign countries. RESULTS: One of recent demonstration trials of telemedicine began in July 2015 in the Korea. The plan was to conduct an emergency telemedical treatment trial among the cooperative medical service centers until the end of February 2016. No telemedical services were provided at the level of local primary care clinics, and there was 1.2% provision at the hospital level. The Europe Union and the United States had more active telemedical services in comparison to the Korea. CONCLUSIONS: The introduction and usage of telemedicine in the Korea was behind those of other countries. It is necessary to develop a proactive support policy for telemedicine through a government implemented trial.

Prevalence of diagnosed and undiagnosed diabetes and prediabetes in New Zealand: findings from the 2008/09 Adult Nutrition Survey.

AIM: To describe the prevalence of diagnosed and undiagnosed diabetes and prediabetes for New Zealand adults. METHODS: The 2008/09 New Zealand Adult Nutrition Survey was a nationally representative, cross-sectional survey of 4,721 New Zealanders aged 15 years and above. Self-reported diabetes and the 2010 American Diabetes Association cutoffs for HbA1c were used to define diagnosed diabetes, undiagnosed diabetes and prediabetes. Prevalence rates were calculated and age-specific diagnosed diabetes rates were compared with those from the Virtual Diabetes Register. RESULTS: Overall, prevalence of diabetes was 7.0%, and prevalence of prediabetes 18.6%. Prevalence of diabetes was higher in men (8.3%, 95% CI: 6.4, 10.1) than in women (5.8%, 95% CI: 4.7, 7.0), and was higher among the obese (14.2%, 95% CI: 11.6, 16.9) compared with the normal weight group (2.4%, 95% CI: 1.4, 3.6). Prevalence of undiagnosed diabetes was highest among Pacific people (6.4%, 95% CI: 3.8, 9.1) compared with Maori (2.2%, 95% CI: 1.2, 3.1) and New Zealand European and Others (1.5%, 95% CI: 0.9, 2.1). CONCLUSION: The high prevalence of prediabetes indicates the prevalence of diabetes will continue to increase in New Zealand. Implementation of effective evidence-based prevention strategies is required to reduce the increasing costs of the diabetes epidemic.