Impact of the health insurance scheme for stateless people on inpatient utilization in Kraburi Hospital, Thailand

Figures & data

Table 1 Characteristics of the HI-PCP and the UCS

Characteristics	UCS	HI-PCP
Population size	47 million	~450,000
Financing source	General tax	General tax
Governing body	NHSO, an autonomous agency regulated by the MOPH	Health Insurance Group, MOPH
Payment mechanism	Capitation for outpatients (~2,800 Baht or US$ 85 per capita) and global budget plus DRGs for inpatients; additional fees for specific high priority services; no copayment by beneficiaries	Capitation for outpatients (varying by year, between 1,000 and 2,000 Baht [US$ 30–61] per capita) and global budget plus DRG for inpatients; no copayment by beneficiaries
Benefit package	Comprehensive: outpatient, inpatient, accident and emergency, high-cost care (including chemotherapy, antiretroviral drugs for HIV/AIDS, renal replacement therapy, organ transplants, etc), and health promotion	Comprehensive, similar to the UCS: outpatient, inpatient, accident and emergency, high-cost care with minimal exclusions (such as organ transplants), and health promotion
Contracted health facilities	All public health facilities under the MOPH, majority of non-MOPH public facilities, some private hospitals, and community clinics voluntarily contracting with the NHSO	Almost all public health facilities under the MOPH

Note: Data from Tangcharoensathien et al⁵ and the Health Insurance Group, the Ministry of Public Health.¹²

Abbreviations: DRG, diagnostic-related group; HI-PCP, Health Insurance for People with Citizenship Problems; MOPH, Ministry of Public Health; NHSO, National Health Security Office, UCS, Universal Coverage Scheme.

Table 2 Provinces with the largest proportion of stateless-to-Thai population

Province	Stateless (N)	Thai (N)	Stateless-to-Thai (%)
1. Ranong	23,486	183,276	12.8
2. Tak	48,588	525,684	9.2
3. Mae Hong Son	18,768	242,742	7.7
4. Chiang Mai	122,340	1,640,479	7.5
5. Kanchanaburi	54,101	938,776	6.4

Note: Data from Suphanchaimat et al.¹⁴

Table 3 Detailed explanation of variables included in the DID model

Variable	Type	Included as	Remark or justification
Admissions/person/year	Continuous	Dependent variable	Newborn admissions were excluded to avoid duplication with delivery admissions
HI-PCP	Binary	Independent variable	HI-PCP was an interaction between insurance and time variables. It reflected the policy effect independent of the time trend had the policy not occurred. It was coded 1 if a patient was stateless and was admitted to hospital during 2011–2012. It was coded 0 if the admission was made by a non-stateless patient (either a UCS or an uninsured patient) at any time or by a stateless patient in 2009.
Insurance	Indicator	Covariate	Insurance variable was added to capture difference in baseline characteristics of beneficiaries between insurance schemes.
Time	Binary	Covariate	Time variable was included to capture effect of natural time trend in the absence of the policy.
Age group	Indicator	Covariate	Age group was used instead of age in years because the age distribution was not quite positively skewed (the mean of age was much lower than its median). Thus, using age group instead of age in years fitted the model better and rendered a more sensible interpretation of the results.
Disease condition	Binary	Covariate	DRGs were used as an indicator of disease severity of an admission in a given fiscal year. The DRGs contained five digits with the fifth digit indicating disease severity. The fifth digit of 1 referred to mild morbidity, whereas 2, 3, and 4 meant moderate, severe, and very severe, respectively. For any admission in a given fiscal year, a patient given a DRG with the fifth digit of 2 or above was coded as 1 (ever had catastrophic illness).
Proximity	Binary	Covariate	A patient with an address in the same district of the hospital was coded as 1 (proximity). This variable was added into the model to mitigate bias from lacking data of patients who did not attend the hospital because of the change in domicile or opting to visit another hospital.

Abbreviations: DID, difference-in-difference; DRG, diagnostic-related group; HI-PCP, Health Insurance for People with Citizenship Problems; UCS, Universal Coverage Scheme.

Figure 1 Number of inpatient admissions by insurance type across years.

Abbreviations: UCS, Universal Coverage Scheme; HI-PCP, Health Insurance for People with Citizenship Problems.

Table 4 Demographic profiles and common diseases in each patient group

	Uninsured (N=796)	Stateless (N=259)	UCS (N=6,098)
Male, n (%)	237 (30.1)	90 (34.8)	2,997 (49.2)
Mean age, years (SD)	30.0 (14.4)	52.6 (22.7)	34 (26.0)
Top three most common diagnoses by ICD10, n (%)	1. O800: normal delivery, 200 (25.1) 2. A09: gastroenteritis, 47 (5.9) 3. O700: first-degree perineal tear, 42 (5.3)	1. J441: COPD, 49 (18.9) 2. O800: normal delivery, 20 (7.7) 3. J46: status asthmaticus, 13 (5.0)	1. A09: gastroenteritis, 606 (10.3) 2. A279: leptospirosis, 371 (6.1) 3. J441: COPD, 343 (5.6)
Catastrophic illness, n (%)	23 (2.9)	10 (3.9)	307 (5.0)
Proximity, n (%)	167 (23.0)	87 (33.6)	1,210 (19.9)

Abbreviations: COPD, chronic obstructive pulmonary disease; ICD10, International Classification of Diseases version 10; SD, standard deviation; UCS, Universal Coverage Scheme.

Table 5 Changes in inpatient utilization by stateless patients before and after the implementation of the Health Insurance for People with Citizenship Problems – results from Student’s t-test

Admissions/person/year	Mean	Standard error	95% Confidence interval
Before policy (1)	1.2	0.1	1.1	1.4
After policy (2)	1.4	0.1	1.2	1.7
Difference (2) − (1)	0.2	0.2	−0.2	0.6

Note: P-value from Student’s t-test=0.305.

Table 6 Impacts of the HI-PCP and other attributes on inpatient utilization by the Poisson regression DID model

	Marginal effecta	Standard error	P-Value	95% confidence interval
Stateless patientsb	0.083	0.069	0.227	−0.052	0.218
UCS patientsb	0.104	0.014	<0.001	0.075	0.132
Post-policy vs pre-policy	0.021	0.019	0.249	−0.015	0.058
HI-PCP policy effect	0.133	0.112	0.234	−0.086	0.352
Age group (<15 years as the reference)
16–30	−0.040	0.017	0.022	−0.074	−0.006
31–45	−0.017	0.031	0.588	−0.078	0.044
46–60	0.077	0.037	0.036	0.005	0.149
>60	0.308	0.048	<0.001	0.213	0.402
Ever vs never had catastrophic illness	0.164	0.046	<0.001	0.075	0.254
Proximity vs non-proximity	0.119	0.040	0.003	0.041	0.198

Notes:

a Absolute change in number of inpatient admissions per year from that in the reference category.

b Compared with the uninsured patients.

Abbreviations: DID, difference-in-difference; HI-PCP, Health Insurance for People with Citizenship Problems; UCS, Universal Coverage Scheme.

Table 7 Impacts of the HI-PCP and other attributes on inpatient utilization by the ordinary least squares DID modela

	Marginal effectb	Standard error	P-Value	95% confidence interval
Stateless patientsc	0.075	0.071	0.290	−0.064	0.215
UCS patientsc	0.098	0.014	<0.001	0.071	0.125
Post-policy vs pre-policy	0.021	0.018	0.247	−0.015	0.057
HI-PCP policy effect	0.149	0.128	0.242	−0.100	0.399
Age group (<15 years as the reference)
16–30	−0.040	0.017	0.022	−0.073	−0.006
31–45	−0.018	0.031	0.571	−0.079	0.044
46–60	0.077	0.037	0.038	0.004	0.149
>60	0.312	0.049	<0.001	0.216	0.409
Ever vs never had catastrophic illness	0.182	0.054	<0.001	0.076	0.288
Proximity vs non-proximity	0.124	0.043	0.004	0.040	0.208

Notes:

a R²=0.043.

b Absolute change in number of inpatient admissions per year from that in the reference category.

c Compared with the uninsured patients.

Abbreviations: DID, difference-in-difference; HI-PCP, Health Insurance for People with Citizenship Problems; UCS, Universal Coverage Scheme.

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