The
following results section provides empirical insights into the linkage between
public health insurance coverage, inpatient utilization pattern, and out of
pocket (OOP) expenditure?by poor households in Alappuzha district. This
analysis, drawing on primary hospital-based data across public and private
institutions, provides insights into who uses inpatient services, where
they?seek care, and the degree of financial protection attained. We present the
findings first detailing the socio-economic and?clinical profile of patients
who were hospitalized, then an evaluation of use patterns and lastly the
incidence and extent of inpatient OOP expenditure. These findings, taken
together, give us a carefully calibrated?view of the way insurance functions in
a mixed public-private health system, and the extent to which it alleviates
financial burden for the poor.
Socio-economic
and clinical profile of the study population
This
section presents the socio-economic, demographic, and clinical characteristics
of the hospitalized poor patients included in the study and explains how these
characteristics vary by insurance status and type of healthcare facility.
Understanding this baseline profile is essential to interpret differences in
utilization and out-of-pocket (OOP) expenditure observed in subsequent
analyses. The results are derived from descriptive analysis of primary data
collected from public and private hospitals in Alappuzha district. The study
sample consisted of poor patients admitted to Government Medical Colleges,
Taluk General Hospitals, and private hospitals. A substantial proportion of the
sample belonged to older age groups, reflecting the higher likelihood of hospitalization
among the elderly. Female patients constituted a slightly higher share of
admissions, particularly in public hospitals, while male patients were more
represented in private hospital admissions. Educational attainment was
generally low across the sample, with a significant share having not completed
secondary education, underscoring the socio-economic vulnerability of the study
population. Scheduled Castes, Scheduled Tribes, and Other Backward Classes
together accounted for a large majority of patients, although representation
varied across hospital types.
Chronic
illnesses such as cardiovascular conditions, diabetes, respiratory diseases,
and renal disorders were common, particularly among patients admitted to
medical colleges and private hospitals. The prevalence of chronic conditions
was higher among insured patients, suggesting that insurance coverage may
facilitate access to inpatient care for conditions requiring prolonged or
repeated treatment. Length of hospital stay varied significantly across
provider types, with longer average stays observed in public medical colleges
compared to taluk hospitals, while private hospitals showed shorter but more
intensive treatment episodes. The following table presents the
socio-demographic and clinical profile of insured and uninsured patients by
type of hospital. The descriptive statistics indicate that poor patients
exhibit socio-economic and clinical gradients in hospitalization patterns based
on insurance status and provider type. Insured patients predominantly utilize
public hospitals, especially government medical colleges, whereas uninsured
patients favor private hospitals, highlighting that insurance reduces financial
barriers to public care but does not fully eliminate private care reliance.
Older individuals, particularly those insured and admitted to public hospitals,
are more frequently hospitalized due to higher morbidity related to age. Gender
differences emerge, with female patients often admitted to public hospitals and
male patients more common in private settings. Insured patients show a higher
prevalence of chronic illness, suggesting insurance facilitates access to
necessary inpatient care, whereas uninsured patients may defer treatment.
Longer hospital stays for insured patients can be attributed to case severity
and clinical management differences. Additionally, admission to paying wards is
largely found in private hospitals and among uninsured patients, illustrating
the financial burdens of hospitalization. The sample shows low educational
attainment and social disadvantage, emphasizing the structural vulnerabilities
of the population and the need for financial protection mechanisms.
The
patterns observed in Table 1 indicate that insurance coverage shapes access and
utilization more strongly than it shape financial outcomes (Table 1). While
insured patients are better able to access public hospitals and longer
inpatient care, their socio-economic vulnerability and clinical needs expose
them to continued OOP expenditure. The strong presence of uninsured poor
patients in private hospitals highlights persistent gaps in public sector
accessibility, referral mechanisms, and perceived quality of care. These
baseline characteristics underscore the importance of adjusting for
demographic, clinical, and provider-level factors in multivariate analysis and
set the foundation for examining why insurance coverage alone does not
translate into comprehensive financial risk protection.
Insurance
coverage and patterns of inpatient utilization
This
section examines how public health insurance coverage is associated with
patterns of inpatient service utilization, focusing on type of hospital used
and length of stay. The analysis addresses how insurance influences access and
utilization and the extent to which this translates into differential use of
public versus private healthcare facilities. Table 2 illustrates the effects of
insurance status on inpatient hospital utilization, showing that public health
insurance significantly increases access to public hospitals while uninsured
patients often resort to private facilities (Table 2). Insured patients
experience longer hospital stays, likely due to greater disease severity and
hospital discharge practices, contrasting with the shorter stays typical in
private care. Insured patients also show more admissions for chronic
conditions, while uninsured individuals frequently face emergency admissions,
indicative of delayed care. Additionally, insured patients benefit from
structured referral pathways in public hospitals, whereas uninsured patients
demonstrate a fragmented care experience in private hospitals.
The
study reveals that insured poor patients predominantly use public sector
hospitals, especially government medical colleges and taluk hospitals, unlike
their uninsured counterparts. Insurance facilitates access to higher-level
public facilities for specialized care. Despite this, some insured patients
still prefer private hospitals, highlighting the private sector's significant
role in Kerala's health system. Hospital stays lengths differed by insurance
and provider type, with insured patients generally staying longer in public
facilities due to illness severity and lack of discharge incentives. In
contrast, private hospitals had shorter stays but delivered more intense
services, while taluk hospitals managed fewer complexities, resulting in
shorter stays.
Incidence
of inpatient out-of-pocket expenditure
This
section analyses the likelihood of incurring any out-of-pocket expenditure
during hospitalization and explains how insurance coverage and provider type
influence this probability. The results are based on binary logistic regression
analysis, controlling for socio-demographic, health-related, and provider-level
factors (Table 3). The regression analysis shows that public health insurance
significantly lowers the chance of incurring out-of-pocket (OOP) costs during
hospitalization, with insured patients being 26% less likely to make any OOP
payments compared to uninsured poor patients. Nonetheless, this effect is
largely diminished by provider and clinical factors. Treatment in private
hospitals is the strongest predictor of OOP expenditures, with patients facing
over three times the odds of incurring costs compared to those in public
hospitals, regardless of insurance, illness severity, and length of stay.
Chronic illness increases the likelihood of OOP expenses due to higher care
demands, while each additional day in the hospital raises the odds of spending
by 9%. Additionally, admission to paying wards is linked to nearly threefold
greater chances of OOP costs. Age and education positively influence the
likelihood of incurring additional costs, whereas gender does not statistically
affect financial exposure during hospitalization.
The
data shows that public health insurance in Alappuzha district offers limited
financial protection against out-of-pocket (OOP) expenses for the poor.
Although it lowers the chances of OOP payments, this benefit is overshadowed by
extensive reliance on private healthcare, prolonged hospital stays, and
uncovered services. Therefore, the current insurance model mainly facilitates
access rather than providing substantial financial security. The influence of
private hospitals indicates that effective universal health coverage (UHC)
strategies require enhanced regulation of these providers and an increase in
public sector capacity. The ongoing OOP payments for insured patients highlight
deficiencies in the benefit packages, particularly in areas like medicines and
diagnostics. To achieve meaningful financial risk protection, reforms must
focus on integrating public provision, expanding benefits, and regulating
markets, rather than solely increasing insurance enrolment.
Magnitude
and determinants of inpatient out-of-pocket expenditure
This
segment examines the magnitude of inpatient OOP expenditure and identifies the
key factors driving cost variation among hospitalized poor patients. The
analysis focuses on how much patients spend out of pocket and how insurance
coverage interacts with provider choice and clinical characteristics to shape
expenditure levels. The findings presented in Table 4 indicate that inpatient
out-of-pocket (OOP) expenditures for poor patients are primarily influenced by
the type of healthcare provider, the exclusion of certain service components
from insurance coverage, and the complexity of clinical cases (Table 4).
Statistically,
insured patients experience approximately 19–23 percent lower OOP spending
compared to those uninsured when controlling for other variables, although this
reduction is less significant compared to provider-related factors. Patients
treated in private hospitals incur substantially higher OOP expenses, with
costs more than doubling those of patients in public hospitals, even when
accounting for other variables. The financial impact further escalates for
patients admitted to paying wards, indicating a direct pricing system that
shifts costs to patients. Chronic illness patients face significantly higher
OOP costs due to increased requirements for medicines, diagnostics, and
extended treatment durations. Each additional day in the hospital corresponds
to roughly a 6 percent rise in OOP expenditures, compounding the financial
strain on patients. Furthermore, expenditures on medicines and diagnostics
acquired outside hospital services are identified as major contributors to OOP
costs, signaling crucial deficiencies in both insurance benefit frameworks and
public hospital supply chains. While age has a minimal positive correlation
with OOP expenditure, education levels show only a slight positive influence,
suggesting minor variations in the intensity of treatment and choices of
services.
Although
insurance does reduce OOP spending to some degree, the main movers of
expenditure?are structural characteristics of the health system—in particular,
the role of private hospitals and the non-coverage of effective coverage
medicines and diagnostics. The close link between?private sector care and
elevated OOP expenditures highlights the limited reach and regulatory power of
public health insurance plans in mixed healthcare markets. The results indicate
that the benefit of insurance must be broadened to involve medication and
diagnostics, public hospital supply chains need to be?bolstered and private
healthcare pricing and practices should be tightly regulated if the prospect of
an effective financial risk protection is to have any real meaning. If these
systemic factors are not addressed insurance-led approaches threaten to enhance
access yet expose poorer households to continued and often large OOP outlays
ultimately jeopardizing progress?towards equitable Universal Health Coverage.