ORIGINAL ARTICLE

Pdf version

A COMPARISON OF MORBIDITY PATTERNS IN PUBLIC AND PRIVATE PRIMARY CARE CLINICS IN MALAYSIA

O Mimi¹ MFamMed (UM); SF Tong² MMed (Fam Med UKM); S Nordin³ MPH (UP Manila), MPHM (Mahidol); CL Teng⁴ MFam Med (UM); EM Khoo⁵ MD, MRCGP; A Abdul-Rahman³ MScPH (Lon); AH Zailinawati⁶ MFamMed (Monash); VKM Lee⁴ MFamMed (UKM); WS Chen⁷ FamMed (ECU); WM Shihabudin³ MBBS, DPIM; MS Noridah⁸ MPH; ZE Fauziah⁸ MPH
¹Klinik Kesihatan Kelana Jaya (Mimi Omar)
²Department of Family Medicine, Universiti Kebangsaan Malaysia (Tong Seng Fah)
³Institute for Health System Research (Nordin Saleh, Abdul Kareem Abdul Rahman, Wan Mohd Shihabudin Wan  Mamat)
⁴International Medical University (Teng Cheong Lieng, Verna Lee Kar Mun)
⁵Department of Primary Care, University of Malaya (Khoo Ee Ming)
⁶Klinik Keluarga Wangsa Maju (Zailinawati Abu Hassan)
⁷Klinik Alam Medic (Chen Wei Seng)
⁸Ministry of Health (Noridah Mohd Salleh, Fauziah Zainal Ehsan)

Address for correspondence: Dr Mimi Omar, Klinik Kesihatan Kelana Jaya, Jalan SS6/3A, 47301 Kelana Jaya, Selangor, Malaysia. Tel: 603-7804 5001, Fax: 603-7804 8475, Email: drmimiomar@gmail.com

Funding: Ministry of Health Research Grant.

Conflict of interest: None.

INTRODUCTION

In Malaysia, primary healthcare is provided by both public and private healthcare providers. The public healthcare sector is heavily funded by the government and patients pay a nominal sum for treatment while the private healthcare services is fee for service where patients pay out-of-pocket themselves, or it is funded by  their employers or by insurance companies. As payment system has been shown to influence patient mix,¹ patient morbidity pattern may thus differ in these two healthcare systems.

Morbidity pattern denotes healthcare utilisation and it is important for healthcare resources allocation and planning. Data for morbidity has been collected from public hospitals in Malaysia since 1960’s and International Classification of Diseases (ICD-10) has been used for disease coding in public hospitals and health clinics since 1999. However, in primary care settings, disease coding using the International Classification of Primary Care (ICPC) is more appropriate as some consultations such as health screening is difficult to classify under the ICD-10. Another method of examining healthcare utilization is determining patients’ reasons for encounter (RFE), which indicate patients’ health needs and concerns.

In 2009, the Malaysian government announced a possibility of merging the public and private health clinics where private sectors may be reimbursed by the Health Ministry for seeing patients from public sector.² It is therefore vital to know the morbidity pattern of both public and private sectors for a comprehensive primary healthcare database as well as healthcare planning. To date, few studies examined or compared this pattern and the services rendered at public and private primary care clinics.^3,4

Therefore, we aimed to determine and compare the morbidity patterns in public and private primary care clinics, determine patients’ RFE and diagnoses using the International Classification of Primary Care, second edition (ICPC-2)^5,6 and rank and compare ten most common diagnoses and RFEs in the two settings. It is hoped that information gathered from this study will assist in the planning of financial and resource allocation for the public-private integration.

METHODS

This paper is part of a cross-sectional study on the ‘Evaluation of the Quality of Public and Private Primary Care in Malaysia’ that was carried out in March and April 2008.The study sample was derived by a simple random sampling technique using SPSS version 15. The study population was all primary health care facilities in Malaysia which was stratified into public and private sector. The list of public clinics was obtained from the Malaysian Ministry of Health (Information and Documentation Unit, Distribution of Public Health Facilities), while the list of private clinics was obtained from the Malaysian Medical and Health Directory 2007. Hospital-based outpatient clinics including university hospitals, specialist clinics other than family medicine specialist clinics and clinics without resident doctors were excluded from the study frame. After exclusion, there were 4482 and 497 private and public primary care clinics respectively. The sample size estimation was calculated using Epi-Info version 6 with the assumptions of 50% prevalence rate of the variables of interest, power of 80%, and 95% confidence level. The sample size was also adjusted to accommodate for an estimated 20% non-response rate. The final study sample was 114 and 100 for private and public primary care clinic respectively.

The selected doctors were approached by telephone and the researchers met them personally to invite and explain about the study. After obtaining consent, the manual and questionnaires were given to the doctors.

In each clinic, Patient Encounter Record (PER) forms was used to collect information on patient profiles, their reasons for visits and diagnoses. The patients were selected using systematic random sampling over seven consecutive working days. As the numbers of daily encounters were high for each doctor in a public clinic, every tenth patient encounter was selected whereas in private clinics, every fifth patient encounter was selected. For each patient encounter, the PER form was completed by the doctor who attended to the patient at the time of consultation or immediately after the consultation to avoid recall bias. The doctor was asked to document the actual reasons for visit, which might not be the chief complaint.

The operational definition of an encounter is a face-to-face interaction between a patient and a doctor in which the doctor renders professional service in response to the explicit or implicit request by the patient or his/her agent. Telephone, on-line consultations or consultations that occur outside a doctor’s usual consultation or treatment room was not considered an encounter. Each patient’s visit to a doctor was an encounter regardless of the number of reasons he/she might have. If the same patient made a second visit on the same day for reasons not related to the first visit, he/she was considered to have two encounters. It was considered to be one encounter if the reasons were related. Consultations that occurred on different days were considered different encounters.

The RFE and diagnoses ^5,6 were coded by trained research assistants and checked for accuracy by the researchers. Data were analysed using SPSS version 15.0. Chi-square test was used for associations between categorical data and student-t-test for continuous data. A statistical significant level was set at p <0.05.

Ethical approval was obtained from the Research and Medical Ethics Committee, Ministry of Health (KKM/NIHSEC/08/0804/MRG-07-LOI-HSR-04).

RESULTS

Health Facility Profiles

Out of the 214 clinics selected to participate, 82 out of the 100 public clinics and 38 out of the 114 private clinics responded. The response rate was 82.0% and 33.3% respectively. The reasons for non-participation were 'not interested', 'too busy', 'unhappiness with the Ministry of Health policies on private clinics', 'do not wish to participate in paperwork' and 'clinic closed down'. About 78% of the public clinics were located in rural and sub-urban areas whilst 60.5% of the private clinics were located in urban areas (ᵪ²=18.504, p<0.001). Mainly doctors in charge of the public clinics (90.2%) or owners of the private clinics (73.7%) contributed information on their clinic facilities. The mean age of doctors at the public clinics was younger than the private clinics (34.8 years versus 48.9 years respectively; t=7.813, p<0.001). About 86.8% of the private clinics were solo practices.

Patient Profiles

Malay patients were the main attendees in both sectors, followed by Indian patients in public clinics and Chinese patients in private clinics. There was a significance difference (p<0.05) in ethnic groups seen in these two settings. Patients attending public clinics were older and more likely to be female when compared to the private clinics (Table 1). One third of patients seen in both sectors were new patients (Table 2). Almost all patients (98.6%) from private clinics were walk-ins and had attended the clinic before while 31.2% of the patients attending public clinics had appointments. Most patients were between 20 to 49 years of age (Table 3). There were more male patients (57.1%) for age group 0 to 19 years old in both settings, but more females (59.2%, p<0.05) for  age group 20 to 65 years.

Table 1: Characteristics of patients recruited at public and private clinics

		Public clinics, n (%)	Private clinics, n (%)	Total, n (%)	Statistical test, p-value
Mean age (yrs) (SD)		37.3 (21.6)	31.4 (19.8)	36.0 (21.4)	t =7.813, <0.001
Ethnicity	Malay	2388 (67.3)	464 (45.5)	2852 (62.4)	ᵡ2=371.77, p<0.001
	Chinese	511 (14.4)	378 (37.1)	889 (19.5)
	Indian	534 (15.0)	78 (7.6)	612 (13.4)
	Others	117 (3.3)	100 (9.8)	217 (4.7)
	Total	3551 (100)	1020 (100)	4571 (100)
Sex	Male	1585 (43.9)	540 (52.7)	2125 (45.9)	ᵡ2=25.11, p<0.001
	Female	2026 (56.1)	484 (47.3)	2510 (54.1)
	Total	3611 (100)	1024 (100)	4635 (100)

Table 2: Type of visit 

Type of visit	Public clinics*n=3512 n (%)	Private clinics*n=1013 n (%)	Total* n=4525 n (%)
Walk in patient with first encounter	1025 (29.2)	284 (28.0)	1309 (28.9)
Walk in patient with previous encounter	1255 (35.7)	715 (70.6)	1970 (43.5)
By appointment	1097 (31.2)	13 (1.3)	1110 (24.5)
Referred	152 (4.3)	1 (0.1)	153 (3.4)
Total	3529 (100)	1013 (100)	4542 (100)

*A patient may be categorized in multiple types of visit

Table 3: Patients attending public and private clinics by age

Age group (years)	Public clinic n (%)	Private clinic n (%)	Total n (%)
Less than 10	447 (12.4)	194 (18.9)	641 (13.8)
10-19	407 (11.3)	82 (8.0)	489 (10.5)
20-29	582 (16.1)	205 (20.0)	787 (17.0)
30-39	511 (14.1)	222 (21.6)	733 (15.8)
40-49	471 (13.0)	130 (12.7)	601 (12.9)
50-59	525 (14.5)	105 (10.2)	630 (13.6)
60-69	412 (11.4)	45 (4.4)	457 (9.8)
70 and above	262 (7.2)	43 (4.2)	305 (6.6)
Total	3617 (100)	1026 (100)	4643 (100)

Morbidity Patterns

A total of 4262 encounters were recorded, 3474 (81.5%) from public clinics and 788 (18.5%) from private clinics. The total RFE was 7280, with 5865 (80.6%) from public clinics and 1415 (19.4%) from private clinics. The mean number of RFE was higher in the public than private clinics but the mean number of diagnoses per encounter was higher in the private than public clinics.

The three most common groups of RFE in public clinics were problems related to respiratory system, “general and unspecified” and cardiovascular system, while for private clinics, they were problems related to respiratory system, “general and unspecified” and digestive system (Table 4). Common RFE in public clinics were chronic diseases such as hypertension and diabetes whereas in private clinics, acute problems such as upper respiratory tract infection and fever were common complaints (Table 5). Acute problems were the main complaints for age group 0 to 49 years while chronic diseases were mainly seen in those aged more than 50 years old.