NUMBER OF PHYSICIANS AND REQUIREMENT FOR HEALTH CARE SERVICES: SYSTEMATIC MODELLING

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KAUNAS UNIVERSITY OF MEDICINE

Jurgita Buivydienė

NUMBER OF PHYSICIANS

AND REQUIREMENT

FOR HEALTH CARE SERVICES:

SYSTEMATIC MODELLING

Summary of Doctoral Dissertation Biomedical Sciences, Public Health (10 B)

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Dissertation was prepared in 2005–2009 at the Department of Preventive Medicine, Kaunas University of Medicine.

Scientific Supervisor

Dr. Liudvika Starkienė (Kaunas University of Medicine, Biomedical Sciences, Public Health – 10 B)

Consultant

Prof. Dr. Habil. Žilvinas Padaiga (Kaunas University of Medicine, Biomedical Sciences, Public Health – 10 B)

Dissertation is defended in the Public Health Research Council, Kaunas Univer-sity of Medicine:

Chairman

Prof. Dr. Habil. Apolinaras Zaborskis (Kaunas University of Medicine, Biomedical Sciences, Public Health – 10 B)

Members:

Prof. Dr. Habil. Jadvyga Petrauskienė (Kaunas University of Medicine, Biomedi-cal Sciences, Public Health – 10 B)

Prof. Dr. Habil. Abdonas Tamošiūnas (Kaunas University of Medicine, Biomedi-cal Sciences, Public Health – 10 B)

Prof. Dr. Habil. Rimvydas Simutis (Kaunas University of Technology, Technolo-gical Sciences, Informatics Engineering – 07 T)

Prof. Dr. Habil. Antanas Verikas (Halmstad University, Technological Sciences, Informatics Engineering – 07 T)

Opponents:

Dr. Rima Kregždytė (Kaunas University of Medicine, Biomedical Sciences, Public Health – 10 B)

Prof. Dr. Violeta Pukelienė (Vytautas Magnus University, Social Sciences, Econo-mics – 04 S)

The dissertation will be defended at the public meeting of the Public Health Research Council, which will be organized in the Symposium hall of the Building for Teaching and Laboratory of Kaunas University of Medicine on 31 August 2010 at 9:30 am.

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KAUNO MEDICINOS UNIVERSITETAS

Jurgita Buivydienė

SISTEMINIS GYDYTOJŲ SKAIČIAUS

IR JŲ TEIKIAMŲ SVEIKATOS

PRIEŽIŪROS PASLAUGŲ

POREIKIO MODELIAVIMAS

Daktaro disertacijos santrauka

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Disertacija rengta 2005–2009 metais Kauno medicinos universiteto Profilaktinės me-dicinos katedroje.

Mokslinis vadovas

dr. Liudvika Starkienė (Kauno medicinos universitetas, biomedicinos mokslai, visuomenės sveikata – 10 B)

Konsultantas

prof. habil. dr. Žilvinas Padaiga (Kauno medicinos universitetas, biomedicinos moks-lai, visuomenės sveikata – 10 B)

Disertacija ginama Kauno medicinos universitete Visuomenės sveikatos mokslo krypties taryboje:

Pirmininkas

prof. habil. dr. Apolinaras Zaborskis (Kauno medicinos universitetas, biomedicinos mokslai, visuomenės sveikata – 10 B)

Nariai:

prof. habil. dr. Jadvyga Petrauskienė (Kauno medicinos universitetas, biomedici-nos mokslai, visuomenės sveikata – 10 B)

prof. habil. dr. Abdonas Tamošiūnas (Kauno medicinos universitetas, biomedici-nos mokslai, visuomenės sveikata – 10 B)

prof. habil. dr. Rimvydas Simutis (Kauno technologijos universitetas, technologi-jos mokslai, informatikos inžinerija – 07 T)

prof. habil. dr. Antanas Verikas (Halmstado universitetas, technologijos mokslai, informatikos inžinerija – 07 T)

Oponentai:

dr. Rima Kregždytė (Kauno medicinos universitetas, biomedicinos mokslai, visuo-menės sveikata – 10 B)

prof. dr. Violeta Pukelienė (Vytauto Didžiojo universitetas, socialiniai mokslai, eko-nomika – 04 S)

Disertacija bus ginama viešame Visuomenės sveikatos mokslo krypties tarybos posė-dyje 2010 m. rugpjūčio 31 d. 9.30 val. Kauno medicinos universiteto Mokomojo labo-ratorinio korpuso siumpoziumų salėje.

Adresas: Eivenių g. 4, LT-50009 Kaunas, Lietuva. Disertacijos santrauka išsiuntinėta 2010 m. liepos 20 d.

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ABBREVIATIONS

CVD – cardiovascular diseases

EM – emergency medicine

EU – European Union

FTE – full-time equivalent GP – general practitioner HC – health care

HCS – health care setting

HICL – Health Information Center of Lithuania HRH – human resources for health

MoH – Ministry of Health of the Republic of Lithuania PHC – primary health care

SL – Statistics Lithuania (Department of Statistics to the Govern-ment of the Republic of Lithuania)

SPF – State Patients’ Fund at the Ministry of Health, Republic of Lithuania

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INTRODUCTION

Relevance of the study. Keeping the balance between requirement and

supply in HC system as well as HRH planning are essential functions of HC systems and huge challenges. This is approached by such measures as administration, management, and scientific research. HRH are related with the highest amount of costs in HC system, therefore its planning should be not just the reaction to crisis but rather effective, long-term and continuous process.

In 2000, World Health Organization (WHO) has stated, that HRH are the main component of HC system. The relevance of the issue was empha-sized in 2006, when World health day and annual report of WHO were appointed for HRH. WHO in his „Health for All“ declaration for Europe emphasized the necessity to ensure appropriate HRH supply, training of specialists as well as optimal conditions for work. The requirement for HC specialists is increasing worldwide. HC system of Lithuania is changing: new technologies and treatment methods come to practice, migration is increasing, there are inequalities in geographic and specialty distributions. Additionally, birthrate is decreasing, life expectancy is increasing as well as number of visits to physicians and consumption of HC services. This induces the revision of work in HC settings. There were relatively few studies on HRH development and planning in Lithuania during 1990– 2000. Main authors were Corder DW, Streikus L et al, Virbalis R et al, Petrauskienė J et al, Kairys J. In 2000, one study used Delphi method about the requirement of physicians. After 2000, there were several studies conducted on HRH planning (Lovkytė L et al) as well as national forecasts for physicians‘ supply and requirement in 2015.

The main aim of HRH planning is to ensure that there will be sufficient number of HRH in the country (at regional, municipal, and HCS level). This planning includes estimates of current and future supply as well as future requirement and what factors will influence oversupply or undersupply]. The main aim of present study was to design the package of measures, which through HC system databases would enable to provide HRH forecasts according to requirement of HC services at national, regional, and municipal level.

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Aims and objectives of the study Aim:

To design the package of measures, which would enable to provide forecasts about physicians supply and requirement at national, regional, and municipal level.

Objectives:

1. To estimate the changes in HRH of HC settings during restructuring of health care during 2002–2005.

2. To analyze the changes in number of physicians and services provi-ded by them during 2002–2008.

3. To build a forecast model about physicians and requirement of ser-vices provided by them and validate it using retrospective analysis. 4. To provide pilot prognoses for 2015.

Novelty of the study. In Lithuania as well as in other countries, HRH

planning and forecasts of requirement are conducted at national level. There are different models and methods used for forecasts. In Lithuania like in several other countries HRH planning includes the model applied in Australia – model of supply planning J.Dewdney, where HRH supply is planned using economic, social, and demographic indicators.

Our study proposes new balance model for HC services requirement and supply which enables to make forecasts at regional and municipal level depending on trends of services, demographic changes and other factors influencing the supply and requirement.

This study analyzed supply in terms of HRH as well as services provided for population, structure of HC system and its SVEIDRA database, and pilot study on regional requirement for health services. All these efforts enabled to build the balance model of requirement and supply in HC system.

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1. MATERIAL AND METHODS

1.1. Supply of HRH and infrastructure of HCS: changes in 2002–2005 depending on restructurization

In 2003, the government has approved Strategy of HCS restructuring. Subsequently, municipalities prepared the plans of PHC development and counties prepared the plans of HCS restructuring. The plans approved by MoH in 2003 (later referred to as Plans) included HC supply changes expressed in indicators for every county and municipality. Data were provided by counties and collected by MoH, the reports included both fac-tual and achievements indicators in 2002 and 2005. Supply comprised of HRH indicators (number of GPs, GP municipal coverage, number of phy-sicians specialists, and number of nursing specialists).

Data analysis was performed for counties and Lithuania. Additionally, ba-sed on population density the counties were divided into 2 subgroups – major and minor. Major counties were Vilnius, Kaunas, and Klaipėda, minor – Aly-tus, Marijampolė, Panevėžys, Šiauliai, Tauragė, Telšiai, and Utena. Of note, only major counties have university hospitals and universities that prepare health care specialists. Additionally, major counties provide the services for the whole country, while minor – only for certain counties.

The indicators were standardized for 100,000 population (except GP municipal coverage). The changes of indicators adjusted for plans were calculated using formula:

where: PAC – plan-adjusted change; t1 – factual value in 2002; t2 – factual value in 2005; tp – planned value for 2005.

The pace of changes were expressed using formula:

where: AAPC – average annual percentage change, t1 – factual value in 2002, t2 – factual value in 2005. The formula is identical to the one used by Plieskis et al.

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Changes on indicators reflecting HRH of HCS at national and county le-vels were calculated using formula:

where: PL – change based on factual data, t2 – factual value in 2005, t1 – factual value in 2002.

Changes on indicators reflecting HRH of HCS at national and county levels considering the plans were calculated using formula:

where: Pa – change based on plans, tp – planned value for 2005, t1 – fac-tual value in 2002.

1.2. Number of physicians and services: changes and associations in 2002–2008

One of the subjects generating the requirement for HC services is the population. The population in Lithuania is decreasing, while the number of services in HC increased. This suggests that the increase is determined by requirement changes, therefore age and gender differences were analyzed for evaluation of requirement changes. The number of services for age and gender groups was standardized for 100,000 population. The forecasts of requirement used demographic forecasts developed by SL.

Another subject in HC system are physicians. The data on number of physicians in 2002–2008 was used from HICL database and standardized for 100,000 population. There were calculated determination coefficient for changes of number of physicians during the period and correlation coefficient for influence of major and minor counties to national number of physicians.

The number of services provided by physicians is the intermediate subject of HC system. Data on services were drawn from HICL database and standardized for 100,000 population. Determination and correlation coefficients were calculated as well.

Data on Alytus county (number of physicians and services by age and gender of patients) in 2002–2009 was drawn from SVEIDRA database.

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1.3. Forecasting model for requirement of physicians and services: building and testing by retrospective analysis

Primary data: collection and sources

Since the precision of forecasts strongly depends on validity of primary data, requirement forecasts used the databases of SL, HICL and SVEIDRA – all governed by national bodies. The data used were related with services and number of physicians.

Data processing

Primary data should be processed for forecasting according to available classifications of services and specialties. For Alytus county the data were for 8 periods and 7 main specialties using several classification. The fore-casts at municipal or regional (county) levels take into account the services provided for inhabitants of different administrative entities – then primary data are separated using certain scheme (Figure 1.3.1).

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Figure 1.3.2. Algorithm of services calculation for municipalities and counties

Establishment of model parameters and testing by retrospective analysis

Retrospective analysis of available data enables to establish the parame-ters for HC system modelling M(t) ir K(t), that is functional relationship of requirement of services and workload of physician calculated using MATLAB software. This software enables the description of relationships using large and small databases.

Function of requirement of HC services

Function of requirement of HC services per capita Mt(t‘) is calculated

for every specialty separately using formulas defined in the model. These values are entered to software package (SPSS, MATLAB, and MS Excel) and then it is calculated the theoretical function describing trends of requirement of HC services in certain period.

Testing of regression functions using retrospective analysis

Applicability of regression analysis for forecasting of requirement for HC services was checked in the study which used defined theoretical regression function and actual data on HC services during certain period.

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3. based on I regression function theoretical values are calculated for a whole period of 2002–2009;

4. II regression function is calculated based on 8 years data from

2002–2009;

5. based on II regression function theoretical values are calculated for a whole period of 2002–2009;

6. theoretical values based on I and II regression functions are compared estimating the differences;

7. II regression function values and actual data from 2002–2009

are compared estimating the differences. Workload of physician

Actual average workload of physician per FTE Kt is varying and

depends upon the number of services required by population and FTE available. Therefore, forecasts of FTEs should use theoretical-forecasted workload of physician Kp calculated for future. If it is possible to forecast

changes in workload and FTE depending on factors influencing workload, then forecasted FTE workload based on model would be comprised of sum of functions: theoretical average FTE Kt and factors influencing workload

Kiv(t). In this case we would have full theoretical-forecasted function of FTE workload Kp(t).

1.4. Forecasting of HC services and requirement of physicians in 2015: pilot study

Pilot forecasts were calculated for requirement of HC services and FTE of physicians in Alytus county. They comprised 8 specialties fulfilling prognostic model of the study which includes data on HC services according to age and gender groups and forecasts of population during certain period.

For comparison of different forecasting methods, the forecasts were calculated also for FTE and requirement of services based on the same baseline values:

1. common trends of requirement for HC services and of population changes during the forecasted period;

2. requirement of HC services across age and gender groups at certain periods and population changes during forecasted periods. This is used in NIVEL model.

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Calculations of FTE workload of physician.

FTE workload of GPs, obstetricians gynecologists, cardiologists, urolo-gists, psychiatrists, radiologists was calculated using 3-years average (the values were similar throughout these years), and workload of neurologists and surgeons was calculated using last year available data.

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2. RESULTS

2.1. Supply of HRH and infrastructure of HCS: changes in 2002–2005 depending on restructurization Lithuania

Restructuring plans were aimed at increase of number of GPs and GP municipal coverage as well as number of nurses; however, the number of specialist physicians had to decrease. The changes of those 4 indicators can be seen Table 2.1.1.

Table 2.1.1. Changes in HRH adjusted for restructurization Plans in Lithuania.

Indicator Lithuania

PL, % Pa, % AAPC, % PAC, % Achievement Number of GPs per 100,000 22.5 48.4 7.0 46.5 +/– GP municipal coverage, % 46.4 41.1 13.5 112.8 + Number of specialist

physicians per 100,000 –11.0 –1.4 –3.8 762.9 + Number of nurses per 100,000 –2.4 5.5 –0.8 –44.6 –

Note: PL – factual change, Pa – planned change, AAPC – average annual percentage change; "+" aim achieved (PAC > 95%); "+/–" insufficient change (PAC > 0%); "–" opposite change (PAC < 0%).

All changes have changed towards the aims set in Plans (sufficiently or less) except number of nurses which were set to increase but decreased instead. Evaluation of change pace showed that GP municipal coverage was increasing most steeply, and number of nurses – most slowly. Aims were achieved evaluating GP municipal coverage and number of specialist physicians while number of GPs increased not sufficiently, and number of nurses changed towards opposite direction.

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Counties

Lithuania has 10 counties. One part of results is presented for major and minor counties (Tables 2.1.2.).

Number of GPs was supposed to increase similarly in major and minor counties. Number of nurses had to increase more in major counties than in minor ones, while number of specialist physicians in minor counties had to increase and in major ones – to decrease. Evaluation of AAPC revealed that number of GPs and nurses changed more in minor counties, while GP municipal coverage and number of specialist physicians – in major. The aims set in Plans were mainly achieved in major counties. However, minor counties did not meet the goals concerning number of specialist physicians and nurses, where the trends went towards opposite direction than planned. Table 2.1.2. Changes in HRH adjusted for restructurization Plans in

counties

Indicators Major counties

PL, % Pa, % AAPC, % PAC, % Achievement Number of GPs per 100,000 13.6 49.1 4.4 27.7 +/– GP municipal coverage, % 46.1 45.2 13.5 102.0 + Number of specialist

physicians per 100,000 –12.3 –3.6 –4.3 343.0 + Number of nurses per 100,000 1.8 6.4 0.6 27.6 +/–

Minor counties

Number of GPs per 100,000 35.9 47.1 10.8 76.6 +/– GP municipal coverage, % 43.1 42.8 12.7 100.6 + Number of specialist

physicians per 100,000 –8.3 3.0 –2.8 –273.7 – Number of nurses per 100,000 –8.5 4.1 –2.9 –207.1 –

Note: PL – factual change, Pa – planned change, AAPC – average annual percentage change; "+" aim achieved (PAC > 95%); "+/–" insufficient change (PAC > 0%); "–" opposite change (PAC < 0%).

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2.2. Number of physicians and HC services provided in 2002–2008 Demography

The total population of Lithuania has decreased by 3.2% during 2002– 2008. It is forecasted that during 2008–2015 it will drop by another 2.5%.

Comparison of counties shows that in 2002–2008 most remarkable decrease was observed in Utena county (6.8%), lower changes in Alytus, Panevėžys, Šiauliai, and Tauragė counties (5.2–5.8%), still lower – in Kaunas, Marijampolė, and Telšiai counties (3.6–3.9%). The least negative change was fixed in Klaipėda county (1.5%). In contrast, in Vilnius county the population very slightly increased during 2002–2008 (0.1%).

Demographic forecasts show that in 2008–2015 the population will still decrease, although to lower extent than before. The highest decrease should be expected in Vilnius county (4.2%), the lowest – in Utena county (0.6%). In Klaipėda the change should not exceed 3.4%, Kaunas, Telšiai and Mari-jampolė counties 1.9–2.5%, while in Alytus, Tauragė, and Šiauliai counties up to 1%.

In Lithuania during 2002–2008, population aged 0–18 years decreased by 17.2% in men and 17.3% in women. Middle-aged population (19–44 years old) decreased by 2.5% and 4.6%, respectively. Meanwhile, older subgroups were on increase: 45–64 years old by 7.0% in men and 5.6% in women, and 65 years and older by 5.8% and 8.0%, respectively. The fore-casts show that in 2008–2015 corresponding changes will be: in younger subgroups decrease of 8.2% and 8.4%, 6.8% and 5.8%, and in older subgroups increase of 8.7% and 3.7%, 1.5% and 0.9%.

Analysis of major counties showed, that in 2002–2008 the younger subgroups diminished: 0–18 years olds by 15.3% in men and 15.6% in wo-men and 19–44 year olds by 1.0% and 3.4%, respectively. The older sub-groups increased during the same period: aged 45–64 years by 7.2% and 6.5%, respectively, and aged 65 years and above – by 8.6% and 11.1%, respectively. The corresponding changes in minor counties were as follows: 0–18 year olds – decrease by 19.4% and 19.2%, 19–44 year olds – decrease by 4.5% and 6.3%, 45–64 year olds – increase by 6.8% and 4.4%, and 65 years and older – increase by 2.7% and 4.6%, respectively.

The forecasts for 2008–2015 claim similar trends further on. Thus, in major counties population aged 0–18 years will decrease by 5.7% in men and 5.7% in women, 19–44 years – decrease by 9.8% and 11.0%, 45–64

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follows: 0–18 years – decrease by 11.2% and 11.7%, 19–44 years – de-crease by 2.7% and inde-crease by 1.8%, 45–64 years – inde-crease by 7.9% and 8.5%, and 65 years and older – decrease by 3.2% and 3.6%, respectively. This shows that minor counties will face more dramatic changes during the forecasted period and these will be varying across age and gender groups more than in major counties.

Supply of physicians

In 2002–2008, the number of GPs per 100,000 population has increased by 63.5% in major counties and 40.6% in minor counties. During 2002– 2005 restructuring, these changes in major counties were 43.2% and later only 14.2%. Similarly, the changes in minor counties were more dramatic in 2002–2005 (30.1%) than in 2005–2008 (8.1%). The patterns of change was very similar in major counties, minor counties, and whole country (R² = 0.99). The national changes in number of GPs was determined by major and minor counties almost equally.

The number of GPs per 100,000 population in Alytus county 2002 was very similar to minor counties average (33.8 and 32.2, respectively), while in 2008 Alytus county was below minor counties average by about 10%. The change of GPs during 2002–2008 in minor counties was more expres-sed than in Alytus county (increase of 40.6% and 31.1%, respectively). Corresponding changes in 2002–2005 reached 30.1% and 20.2%, while in 2005–2008 – 8.1% and 9.0%. It can be stated, that Alytus county somewhat lagged several years compared to other minor counties. However, Alytus county correlated with minor counties average very clearly (R² = 0.98).

Number of physicians per 100,000 population in 2002 was 1.8 times higher in minor counties than major and until 2008 this increased to 2 times difference. However, in 2002–2008 the number of physicians was increasing nationally (8.0%) and in major counties (11.3%), but not in minor counties (1.8% decrease). During restructuring of HC system in 2002–2005 this decreased by 0.6%, 0.4% and 2.1%, respectively, while in 2005–2008 it increased by 8.7%, 11.7% and 0.2%, respectively. This shows, that the trends before and after 2005 were quite similar. National changes were followed by major counties considerbaly more than by minor counties, therefore major counties in essence determined the

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Number of services for population

Number of visits to GPs per 100,000 population in 2002 was higher in minor than major counties by 1.1 times, while in 2008 the difference was straight opposite. Number of such visits remarkably increased in 2002– 2008 – by 140% in major counties, 101% in minor counties, and 122% nationally. The corresponding changes during restructuring of 2002–2005 was 109%, 69% and 91%, while in 2005–2008 – increase of only 15%, 19% and 17%, respectively. Thus, the changes took place mainly during the restructuring process, when one of the main priorities was shift to primary care. These trends were quite similar across country and subgroups of counties (R² = 0.99). The national changes were similarly influenced by major and minor counties.

The number of visits to physicians specialists per 100,000 population was higher in major than minor counties by 1.3 times, both in 2002 and 2008. However, the changes in 2002–2008 slightly differed – the number of visits increased by 13.2% in major counties, 18.1% in minor counties, and 15.3% nationally and the variation was very similar. The correspon-ding values of increase for 2002–2005 restructuring period were 6.5%, 5.9% and 6.4%, respectively, and in 2005–2008 – 6.3%, 11.5% and 8.4%, respectively. Thus, even though the changes in minor counties were lag-ging to some extent, the impact of major and minor counties to national changes were similar.

The number of services for population increased (by 111–194%) to following specialists: urologists, cardiologists, infectionists, gastroentero-logists, and nefrologists. Lower increase (22.5–96.9%) was observed in services provided by GPs, pulmonologists, orthopedic traumatologists, endocrinologists, endoscopy and echoscopy specialists, physical therapists and rehabilitation specialists, radiologists, and rheumatologists. Minute changes were for services of catchment pediatricians and therapeutists (–2.6%), obstetricians gynecologists (–5.3%), dermatovenerologists (0.1%), psychiatrists (6.8%), and ophtalmologists (2.5%). Finally, some services decreased – these were services provided by children‘s diseases physicians (56.9%), neurologists (29.0%), surgeons (35.6%), phtysiatrists (84.6%), and otorhynolaringologists (14.4%).

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2.3. Forecasting model for requirement of physicians and services: building and testing by retrospective analysis

In 2002–2009, the requirement for services provided by general practi-tioners, urologists, psychiatrists, and radiologists was changing in logarith-mic trends, services of surgeons – in declining manner. Trends of services provided by obstetricians gynecologists and neurologists were declining exponential, and cardiologists – growing exponential.

2.4. Forecasting of HC services and requirement of physicians in 2015: pilot study

Majority of services provided by GPs will fall to patients aged 0–18 years (18.4% for females and 17.5% for males) as well as 45–64 years (13.8% and 11.3%, respectively) and 65 years and more (15.7% and 12.2%, respectively).

Obstetricians gynecologists will provide services mainly to women

aged 19–44 years (52.7%) and 45–64 years (39.9%).

Cardiologists will provide services mostly to subjects aged 45–64 years

(21.2% for males and females) and 65 years and more (26.1% for males and 19.8% for females).

Urologists services will be provided predominantly to men aged 65

years and more (62.5%). Significantly less services will fall to men aged 45–64 years (23.5%).

Neurologists will serve more to women than to men in elder groups:

45–64 years old women will consume 25.8% of neurologists‘ services while men – 17.1%. Similarly, patients aged 65 years and above will consume 21.5% and 16.6%, respectively.

However, men will consume more services of surgeons. In 2015, men aged 19–44 and 45–64 years will consume 18.9% and 16.1% of total

sur-geons services, respectively, and men aged 65 years and more – 15.4%.

Meanwhile, corresponding numbers for women are 14.5%, 7.7%, and 10.9%.

Psychiatrists will provide services more for women than for men in

population aged 45 years and above, while for younger adults men wil consume more services. Thus, women aged 45–64 years will receive

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Radiologists services will mainly fall to women aged 45–64 and 65 and

more years (19.2% and 19.9%, respectively) and this will exceed corre-sponding requirement of services by men (14..2% and 16.2%, respectti-vely).

The forecasts for Alytus county reveal that the biggest change in requi-rement of services during 2009–2015 will fall to cardiologists – increase by 74.5%. Lower increase is expected for urologists (by 23.4%), psychia-trists (by 17.9%), radiologists (by 13.6%), and GPs (by 7.2%). Opposite trend – decrease of requirement – is forecasted for services of neurologists (by 18.8%), surgeons (by 16.8%), and obstetricians gynecologists (by 12.0%). Similarly, the forecasts of requirement change during 2009–2015 in full-time equivalents will be as follows: increase of requirement for cardiologists (by 86.6%), urologists (by 21.1%), GPs (by 8.2%), radiolo-gists (by 1.0%), and decrease of requirement for obstetricians gynecolo-gists (by 19.0%), neurologynecolo-gists (by 18.8%), surgeons (16.8%), and psychia-trists (5.4%).

The comparison of forecasts for 2015 shows that our model compared to other models including population size and functions of requirement shows slightly lower number of required physicians of such specialties: GPs, urologists, neurologists, surgeons, psychiatrists, radiologists, and considerably lower – cardiologists. Additionally, comparing forecasts for 2015 using our model compared with NIVEL model, the requirement is higher for cardiologists, urologists, psychiatrists, radiologists, and GPs, while lower requirement – for neurologists, surgeons, and obstetricians gynecologists.

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CONCLUSIONS

1. Changes in supply of human resources and infrastructure in health care during 2002–2005 show that restructuring of health care settings at counties level was not the only factor influencing supply of infrastructure, therefore not all planned changes took place.

2. In 2002–2008 the population of Lithuania decreased but the number of visits to physicians increased. The changes in number of physicians were influenced by increased requirement (in major counties) and other factors (minor counties).

3. Using our model and nationally available data, forecasts can be made on yearly basis at national, regional (counties), local (municipalities), and health care setting levels with regard to factors influencing supply and requirement. Such forecasts should be included in decision making related with supply of health care workforce.

4. NIVEL methodology compared to our new method poorly reflects the trends of requirement for health care services.

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PUBLICATIONS

1. Buivydienė Jurgita; Starkienė Liudvika; Šmigelskas Kastytis. Health care reform in Lithuania: evaluation of changes in human resources and infrastructure / Jurgita Buivydienė, Liudvika Starkienė, Kastytis Šmigelskas // Scandinavian journal of public health. 2010, vol. 38(3):259-265.

2. Buivydienė Jurgita; Šmigelskas Kastytis; Buivydas Egidijus. Gyven-tojų skaičiaus pokyčių amžiaus ir lyties grupėse įtaka gydyGyven-tojų porei-kiui [Requirement for physicians: influence of population changes in age and gender] / Jurgita Buivydienė, Kastytis Šmigelskas, Egidijus Buivydas // Bendrosios praktikos gydytojas. 2010, vol. 14(7):487-493.

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SUMMARY IN LITHUANIAN

Sveikatos priežiūros sistemoje teikiamų paslaugų poreikio ir jų pasiūlos pusiausvyros užtikrinimas bei SPŽI pasiūlos planavimas yra svarbiausios sveikatos priežiūros sistemos funkcijos ir tuo pačiu sudėtingi uždaviniai, kurių sprendimams pasitelkiamos ne tik administracinės valdymo priemo-nės, bet ir mokslinė tiriamoji veikla. SPŽI taip pat sudaro didžiausią svei-katos priežiūros sistemos išlaidų dalį, todėl jų planavimas turi būti ne pa-vieniai sprendimai ištikus krizei, o efektyvus, ilgalaikis ir nenutrūkstantis procesas.

Tikslas

Parengti prognozavimo priemonių paketą, skirtą gydytojų skaičiaus ir jų teikiamų paslaugų poreikio prognozavimui respublikos, regionų, rajonų ir sveikatos priežiūros įstaigų lygmenyse.

Tiriamieji ir tyrimo metodika

Duomenys buvo renkami SAM, kur kiekviena apskritis pateikė savo Planų įgyvendinimo ataskaitas. Analizei buvo naudoti 2002 m. ir 2005 m. faktiniai duomenys ir lyginti su 2005 m. Planuose numatytais siekiniais. Planuose pasiūla buvo vertinta šiais SPŽI rodikliais: BPG skaičius, BPG aptarnaujamų savivaldybės gyventojų dalis, gydytojų specialistų skaičius, slaugos specialistų skaičius. Gautų duomenų analizė atlikta šalies ir apskri-čių mastu. Alytaus apskrities gydytojų skaičius ir jų suteiktos paslaugos pagal specialybes gautos iš TLK duomenų bazės. Lietuvos, didžiųjų ir ma-žųjų apskričių gydytojų suteiktas paslaugas ir gydytojų skaičius gauti iš LSIC duomenų bazės. Gyventojų skaičius ir gyventojų skaičiaus progno-zės gautos iš SD.

Rezultatai

Pagal restruktūrizacijos Planus didėjo BPG aptarnaujamų savivaldybės gyventojų dalis ir mažėjo gydytojų specialistų skaičius šalies mastu ir didžiosiose apskrityse. Lietuvos mastu 2002–2008 m. gyventojų skaičius mažėjo, o gydytojų suteiktų paslaugų ir gydytojų skaičius didėjo. Progno-zuojama, kad gydytojų teikiamos paslaugos pagal amžiaus grupes ir lytį

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Išvados

SPŽI pasiūlos pokyčiai 2002–2005 m. parodo, kad tuo metu vykęs ap-skričių sveikatos priežiūros įstaigų restruktūrizavimas nebuvo vienintelis SPŽI pasiūlą įtakojantis veiksnys, todėl ne visi restruktūrizavimo Planų įgyvendinimo rodikliai buvo pasiekti. Nuo 2002 m. iki 2008 m. šalies gy-ventojų skaičius mažėjo, o ambulatorinių apsilankymų skaičius pas gydy-tojus kasmet didėjo. Šio tyrimo rezultatai parodė, kad bendro gydytojų skaičiaus pokyčius 2002–2008 m. didžiosiose apskrityse įtakojo augantis gyventojų poreikis sveikatos priežiūros paslaugoms, o mažosiose – ir kiti veiksniai, todėl bendras gydytojų skaičius mažosiose apskrityse 2002– 2008 m. beveik nekito. Per šį laikotarpį ženkliai išaugo BPG skaičius ir gyventojų apsilankymų skaičius pas šiuos gydytojus. Sveikatos priežiūros paslaugų poreikio ir pasiūlos pusiausvyros modelio veikimo patikrinimo rezultatai parodė, kad daugiamatės regresinės funkcijos gali būti taikomos prognozuojant sveikatos priežiūros paslaugų poreikį. Mūsų modeliu pa-rengtos gydytojų skaičiaus poreikio prognozės sudaro galimybes progno-zuoti atskirų specialybių gydytojų skaičiaus poreikį ir naudoti šį modelį respublikos, regionų, rajonų ir sveikatos priežiūros įstaigų lygmenyse, o už gydytojų pasiūlą atsakingos institucijos gali ruošti planus tinkamai gydy-tojų pasiūlai užtikrinti. Palyginus sveikatos priežiūros paslaugų poreikio bandomasias prognozes, parengtas pagal mūsų aprašytą sisteminį sveikatos priežiūros paslaugų poreikio ir pasiūlos pusiausvyros modelio metodą, su prognozėmis, parengtomis pagal olandų modelio NIVEL metodą, progno-zės parengtos pagal mūsų metodą tiksliau atspindi gyventojų poreikį svei-katos priežiūros paslaugoms, nei prognozės, parengtos pagal NIVEL me-todą.

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AUTHOR’s CV

Name, Surname: Jurgita Buivydienė

Address: Department of the Public Health, Kaunas University of Medicine

Eivenių str. 4, LT-50161 Kaunas, Lithuania.

Phone: +370 687 10774

E-mail: buivydiene.jurgita@gmail.com

Education:

1993–1996 Kaunas higher school of medicine, qualification: midwife 1998–2002 Kaunas University of Medicine, Bachelor of Public Health 2002–2004 Kaunas University of Medicine: Master of Public Health

(specialization – kynesiology).

2003–2005 Kaunas University of Medicine: Bachelor of Rehabilitation (qualification – physical therapy)

2005–2009 Kaunas University of Medicine: Doctoral studies

Medical experience:

1990–1992 Laboratory worker at Clinics of Cardiology, Kaunas University of Medicine

1993–2003 Sanitarian at Kaunas University of Medicine Hospital 1996–1999 Midwife at midwifery observation section,

Kaunas 2nd Clinical Hospital

2004–2006 Kynesiology, health center „Sveikieji baltai“

2005 till now Junior research assistant, Kaunas University of Medicine

Research activities:

2005 Report: Buivydienė J. The influence of pregnancy and labour on the pelvic floor muscles function.

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2010 Article: Buivydienė J., Starkienė L., Šmigelskas K. Health care reform in Lithuania: evaluation of changes in human resources and infrastructure. Scand. J. PH. 2010; 38(3):259-265.

2010 Article: Buivydienė J., Šmigelskas K., Buivydas E.

Requirement for physicians: influence of population changes in age and gender. Bendrosios praktikos gydytojas. 2010; 14(7):487-493.

NUMBER OF PHYSICIANS AND REQUIREMENT FOR HEALTH CARE SERVICES: SYSTEMATIC MODELLING

Jurgita Buivydienė