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RESEARCH ARTICLE
Under-utilisation of noncommunicable
disease screening and healthy lifestyle
promotion centres: A cross-sectional study
from Sri Lanka
Thilini HerathID
1
*, Manuja Perera
2
, Anuradhani Kasturiratne
2
1Faculty of Health-Care Sciences, Department of Primary Health Care, Eastern University, Batticaloa, Sri
Lanka, 2Faculty of Medicine, Department of Public Health, University of Kelaniya, Ragama, Sri Lanka
*herathhmtp@esn.ac.lk
Abstract
Background
Healthy Lifestyle Centres (HLCs) are state-owned, free-of-charge facilities that screen for
major noncommunicable disease risks and promote healthy lifestyles among adults older
than 35 years in Sri Lanka. The key challenge to their effectiveness is their underutilisation.
This study aimed to describe the underutilisation and determine the factors associated, as a
precedent of a bigger project that designed and implemented an intervention for its
improvement.
Methods
Data derived from a community-based cross-sectional study conducted among 1727 adults
(aged 35 to 65 years) recruited using a multi-stage cluster sampling method from two dis-
tricts (Gampaha and Kalutara) in Sri Lanka. A prior qualitative study was used to identify
potential factors to develop the questionnaire which is published separately. Data were
obtained using an interviewer-administered questionnaire and analysed using inferential
statistics.
Results
Forty-two percent (n = 726, 95% CI: 39.7–44.4) had a satisfactory level of awareness on
HLCs even though utilisation was only 11.3% (n = 195, 95% CI: 9.80–12.8). Utilisation was
significantly associated with 14 factors. The five factors with the highest Odds Ratios (OR)
were perceiving screening as useful (OR = 10.2, 95% CI: 4.04–23.4), perceiving as suscep-
tible to NCDs (OR = 6.78, 95% CI: 2.79–16.42) and the presence of peer support for screen-
ing and a healthy lifestyle (OR = 3.12, 95% CI: 1.54–6.34), belonging to the second (OR =
3.69, 95% CI: 1.53–8.89) and third lowest (OR = 2.84, 95% CI: 1.02–7.94) household
income categories and a higher level of knowledge on HLCs (OR = 1.31, 95% CI: 1.24–
1.38). When considering non-utilisation, being a male (OR = 0.18, 95% CI: 0.05–0.52),
belonging to an extended family (OR = 0.43, 95% CI: 0.21–0.88), residing within 1–2 km
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OPEN ACCESS
Citation: Herath T, Perera M, Kasturiratne A (2024)
Under-utilisation of noncommunicable disease
screening and healthy lifestyle promotion centres:
A cross-sectional study from Sri Lanka. PLoS ONE
19(4): e0301510. https://doi.org/10.1371/journal.
pone.0301510
Editor: Pracheth Raghuveer, Kasturba Medical
College Mangalore, Manipal Academy of Higher
Education, INDIA
Received: September 26, 2022
Accepted: March 15, 2024
Published: April 4, 2024
Copyright: ©2024 Herath et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
information files.
Funding: This study is funded by the PhD
Scholarships under the Accelerating Higher
Education and Development (AHEAD) project of
the Ministry of Higher Education Sri Lanka and the
World bank. Reference number AHEAD/PhD/R1/
AH/037. The funders had no role in study design,
(OR = 0.29, 95% CI: 0.14–0.63) or more than 3 km of the HLC (OR = 0.14, 95% CI: 0.04–
0.53), having a higher self-assessed health score (OR = 0.97, 95% CI: 0.95–0.99) and low
perceived accessibility to HLCs (OR = 0.12, 95% CI: 0.04–0.36) were significantly
associated.
Conclusion
In conclusion, underutilisation of HLCs is a result of multiple factors operating at different
levels. Therefore, interventions aiming to improve HLC utilisation should be complex and
multifaceted designs based on these factors rather than merely improving knowledge.
Introduction
One of the vital, cost-effective strategies in preventing NCDs is screening and proper manage-
ment of people at risk. The main aim of those services is to prevent the progression of common
NCDs and reduce the associated burden of disease and death [1]. World Health Organization’s
(WHO) essential intervention package recommends integrating with primary care to ensure
the participation of poor and most vulnerable individuals and communities [1]. This strategy
aims to reduce inequality by improving access and affordability for the needy and high-risk
people. However, the success of a screening program is also dependent on public participation
and the reach of the service to the target population [2]. According to previous worldwide stud-
ies, uptake of state screening services by asymptomatic individuals is significantly low [2–5].
Healthy Lifestyle Centres (HLCs) in Sri Lanka, which is the first such initiative in Southeast
Asia, is a response to promote early detection and management of Noncommunicable Diseases
(NCDs) and risk factors at the Primary Health Care (PHC) level [6]. HLCs aim to cater for the
poorest societal segments aiming to prevent Cardio Vascular Diseases (CVDs) [7]. The pri-
mary target population for HLCs is adults aged over 35 years who are not diagnosed with any
form of NCD. Ministry of Health expects HLCs to function at least once per week from 8.00
am to 12.00 noon and the services are offered absolutely free of charge. Self-referral is pro-
moted via health education through posters, banners, leaflets, printed invitations, health talks,
and referrals by field health staff and medical officers. Body mass index (BMI), waist circum-
ference, blood pressure, capillary fasting blood sugar, and total cholesterol are among the mea-
surements taken at the HLCs, and clients will also receive lifestyle modification advice and a
date for the follow-up visit based on the 10-year CVD risk [8].
Similar to the global context, HLCs in Sri Lanka report a low uptake by its potential clients.
Even though HLCs are financially and physically available for all community segments,
according to the data of the pre-pandemic era (2018 and 2019), the reported utilisation rate of
HLCs was only 10.0% and 6.9% respectively across the country. Further, there was substan-
tially low male participation (male-to-female ratio of 1:2.2 (2018) and 1:2.6 (2019)) [9,10]. The
annual utilisation has been reduced further to 3.7% in 2020 and 2.9% in 2021 with the
COVID-19 pandemic [9]. During the first quarter of 2022, the utilisation has further reduced
to 1.5% [11]. Therefore, the overwhelming challenge faced by the HLC service providers is
underutilisation [12–14].
According to the national multisectoral action plan for the prevention and control of NCDs
[15], it is targeted to achieve a 25% relative reduction in premature mortality from cardiovas-
cular disease, cancer, diabetes, or chronic respiratory diseases while targeting a reduction in
NCD risk factors such a 25% relative reduction in the prevalence of raised blood pressure.
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data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
Thus, strengthening HLC utilisation has been identified as a priority area in reorganising PHC
in Sri Lanka to achieve above NCD targets by 2025 [12].
To the best of our knowledge, factors associated with the underutilisation of HLCs by its
target population have not been investigated yet. However, available literature highlighted that
the underutilisation of HLCs might be linked with low publicity about HLCs and perceptions
related to health and wellbeing [13]. Systematic reviews based on other countries suggest that
lower socioeconomic status, male gender, younger age, negative attitudes regarding the out-
come of screening, low self-assessed health score, ongoing frequent or recent consultation at a
general practice, and less social support are important factors of underutilisation of the screen-
ing services [16,17]. Most of the previous studies were done in developed countries, and there
is a dearth of research evidence from developing countries. Hence, the present study aimed to
describe the underutilisation and determine the factors associated with the underutilisation of
HLCs in Sri Lanka.
Methods
Study design and study population
A cross-sectional study in the Gampaha and Kalutara districts, the adjacent two districts to the
capital district of Sri Lanka. The selection of these districts was due to the high service delivery
and availability of resources compared with other districts. Data was collected from May to
June 2019.
The study population was 35- to 65-year-old adults who lived in the selected districts for at
least six months before the data collection period. Individuals who were already diagnosed
with chronic noncommunicable conditions, all three risk conditions (diabetes, hypercholester-
olaemia, hypertension), pregnant and postpartum women (six months) were excluded because
they are not included in the target population of the HLCs.
Sample and sampling method
The sample size (n = 1950) wascalculated based on a standard formula [18] (expected propor-
tion of individuals in the population who utilised the HLCs (p) = 0.255 [19], acceptable degree
of absolute precision (d) = 0.03, level of significance (5%) (Z
1-α/2)
= 1.96
)
, was adjusted for clus-
ter sampling (design effect = 2). Respondents were selected using a five-staged sampling
method.
Stage 1-. The cluster in the primary sampling unit was the catchment area of the HLC. Min-
istry of Health has not defined the catchment areas for HLCs formally and expects it to cover
the catchment areas of the primary health care institutes. Some regional health offices have
defined catchment areas, but they are not uniform across the regions. Therefore, considering
the need for consistency and feasibility, the Village Administrative divisions (named as the
Grama Niladari (GN) divisions, which are the lowest administrative division in Sri Lanka)
within five kilometres from the selected HLCs were considered as the catchment areas for this
study. Thirty catchment areas (15 catchment areas from each district) were selected using the
simple random sampling method.
Stage 2- The cluster in the secondary sampling unit was the Village Administrative (GN)
divisions. The average number of GN divisions in a catchment area of a HLC in the two dis-
tricts was 14. Five GN divisions from each catchment area were randomly selected.
Stage 3- All names of residential blocks or streets (depending on the division) in the selected
division were listed alphabetically with the support of the Village Administrative Officer
(Grama Niladari). Three blocks/streets in a selected GN division were selected using the simple
random sampling method.
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Stage 4- Four/five households were selected systematically from the selected household
block as follows.
Identifying the starting point. A starting point from the selected residential block or
street was selected randomly using the area map available at the GN office. The first household
located at that starting point was visited. If there were no eligible individuals in that household,
that household was excluded, and the adjacent household on the left side was selected. This
was continued until the initiating point was identified.
Selecting the rest of the households. Once the initiation point was identified, the next
three households (four in the third block) were selected using the systematic sampling method.
For this, a sampling frame was prepared for each selected block/street considering the number
of households located in that selected block/street. The sampling interval was calculated using
the aforementioned sampling frame and the number of households that was needed to be vis-
ited in each block. If there was no one at the selected household at the time of data collection,
the particular data enumerator managed to verify the presence of at least one eligible member
in that household, and a message for an appointment was sent to that resident. The data enu-
merator visited three times before it was labelled as a non-response.
Stage 5- Once the data enumerator visited the household, all eligible participants were
listed. An individual among the eligible participants present at the household during the data
enumerator’s visit was selected, applying a simple random sampling method using the lottery
method. If there was only one individual that matched the inclusion criteria in the household,
that respondent was selected automatically.
Data collection instrument and method
A conceptual framework developed by a prior qualitative study by us was used to develop the
interviewer-administered questionnaire used in this study [20]. According to this framework,
HLC utilisation is principally influenced by the client’s cognitive and psychological attributes,
family and community characteristics, and services-related perceptions, along with medical
and screening history.
•HLC utilisation
The outcome measure was the self-reported attendance at HLCs. Data enumerators verified
the response based on the availability of the HLC record book. A binary variable was created
indicating the HLC attendance (yes = 1, no = 0).
•Client’s cognitive and psychological attributes
The client’s cognitive and psychological attributes encompassed knowledge on HLCs (using
2 multiple choice questions on aim and target diseases and 3 best of five questions on target
age, population, and functioning date of the HLC), self-health assessment (using 0–100 rat-
ing scale), perceived susceptibility to NCDs or risk conditions (using five mutually exclusive
responses), perceived usefulness of screening (using five mutually exclusive responses),
enthusiasm on screening (using four items with a 3 point Likert scale), and enthusiasm to
initiate and maintain a healthy lifestyle (using four items with a binary scale (yes and no)).
Depending on the gradient of the responses in the factors of perceived susceptibility to
NCDs or risk conditions and perceived usefulness of screening, each response was catego-
rised into positive and negative perceptions. Perceived susceptibility to NCDs or risk condi-
tions was defined as accurately perceiving the vulnerability to acquiring a common CVD
and the risk conditions in the future.
•Client’s family and community characteristics
Client’s family and community characteristics included perceived family support for
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screening and a healthy lifestyle (using six items with a 3-point Likert scale), acceptance of
negative gender-related norms on screening (using nine items with a 5-point Likert scale),
acceptance of negative norms related to NCDs and screening (using seven items with a 5
point Likert scale), perceived community networking (using nine items binary scale) and
perceived presence of peer support (using 4 mutually exclusive responses to measure each
perceived availability of supportive discussions and motivations) for NCD prevention,
screening and healthy lifestyle. Perceived presence of peer support was classified as presence
and absence, considering the categorisation of the responses of the two attributes.
•Clients’ services-related perceptions
We obtained information on perceived negativity on functioning (using 4 items with binary
responses), perceived quality of services (using a 5-point Likert type scale), and perceived
accessibility to HLCs (using a 5-point Likert type scale).
•Client’s medical and screening history
Data on family and personal history of NCDs or intermediate-risk conditions (presence of
diabetes, hypertensionor hyperlipidemia) (dichotomous no and yes responses) and history
of previous screening experience for blood sugar and cholesterol (dichotomous no and yes
responses) was obtained.
Three experienced public health academics in state universities assessed the questionnaire
for face and content validity. The developed questionnaire was pretested in an adjacent district
(Kurunegala). Ethical clearance for the study was obtained from the Ethics Review Committee
of the Faculty of Medicine, University of Kelaniya, Sri Lanka (P/141/07/2018). The data enu-
merator explained the study purpose and the procedures to the selected participants and
obtained written consent before the data collection.
Data analysis
Awareness and prevalence levels were calculated using descriptive statistics. Initially, the bivar-
iate analysis was conducted using the Fisher Exact test, Mann-Whitney U test and Chi-Square
Statistics to examine statistically significant differences between the utilisation of HLCs and
relevant variables. The variables that were found to be statistically significant by the bivariate
analysis were selected for the multivariable regression analysis. Assumptions were checked
before conducting the adjusted logistic regression analysis and there was no multicollinearity
among variables. Logistic regression models were fitted to estimate adjusted odds ratios of the
utilisation of HLCs with 95% CIs and p-values, Analysis was conducted separately for men
and women to prevent potential bias and identify sex-specific variations. Data analyses were
conducted using the Statistical Package for Social Sciences version 23.
Results
Response rate and characteristics
Of the total 1950 approached, 1727 individuals responded, accounting for a response rate of
88.6%. Of the respondents, 59.8% (n = 1033) were females. The mean age was 49.8 (SD = 8.73)
years (Female 49.71 (SD = 8.59), Male 49.89 (SD = 8.92)). The majority of the female respon-
dents were housewives (n = 788, 76.3%), and most of the male respondents were daily wagers
(n = 212, 30.5%). The mean household income was LKR 38036. 5 (USD 118.34) (SD = LKR
33865.6 (USD 105.36)) with a median household income of LKR 30,000 (USD 93.34)
(IQR = LKR 30,000 (USD 93.34)), ranged from LKR 0 to LKR 600,000 (USD 1866.73)
(Table 1).
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Table 1. Distribution of study participants according to selected socio-demographic and economic
characteristics.
Characteristic (n = 1727) Frequency %
Sex
Female 1033 59.8
Male 694 40.2
Age (Yrs) (Mean = 49.8 (SD = 8.73))
35–44 557 32.2
45–54 592 34.3
55–65 578 33.5
Religion
Buddhist 1547 89.6
Others (Roman Catholic, Hindu, Islam, Christian) 180 10.4
Level of education
No formal education 10 0.6
Primary education (Grade 1–5) 74 4.3
Lower Secondary (Grade 6–9) 1018 58.9
Upper secondary (Grade 10–13) 57.5 33.3
Tertiary (Bachelor and above) 50 2.9
Marital status
Married 1644 95.2
Single 58 3.4
Divorced 04 0.2
Widow 16 0.9
Separated 05 0.3
Employment Category
Stay-home mother 788 45.6
Unemployed (able to work) 77 4.4
Retired (with pension) 70 4.1
Self-employer 229 13.3
Daily wager 240 13.9
Non-government worker 185 10.7
Government worker 138 8.0
Monthly household income (LKR) (mean = LKR 38036. 5 (SD = LKR 33865.6))
0–15,000 376 21.8
15,001–30,000 509 29.5
30,001–40,000 305 17.6
40,001–55,000 212 12.3
55,001–600,000 325 18.8
Number of children (mean 1.95 = (SD = 1.12))
0 157 9.1
1–2 1124 65.1
3–4 429 24.8
More than 5 17 1.0
Type of family
Nuclear 1220 70.6
Extended 507 29.4
Distance to the nearest health facility (km) (mean = 1.76 km (SD = 1.19 km))
0–1 704 40.8
1.1–2 572 33.1
(Continued)
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Prevalence of HLC utilisation
Only 42.03% (n = 726; 95% CI: 39.7–44.4) were aware of the existence of an HLC in their local-
ity or of its services. Of the total 1727 participants, only 195 (11.3%; 95% CI: 9.8–12.8) had uti-
lised HLCs and the utilisation rate was higher among females (n = 165; 15.9%; 95% CI: 13.7–
18.2) compared to males (n = 30; 4.3%; 95% CI: 2.81–5.8).
Factors associated with HLC utilisation
Bivariate analysis (Table 2). HLC utilisation was associated with most of the sociodemo-
graphic and economic characteristics: sex (p <0.001), occupation category (p <0.001), type
of family (p = 0.027), available transport method to travel to HLC (p <0.001), distance from
home to nearest HLC (p = 0.017), status of having a personal income source (p <0.001), and
household income category (p = 0.015). Most of the users were from the female gender
(n = 165, 15.9%; 95% CI: 13.7–18.2, p <0.001) and were housewives (n = 133, 16.9%; 95% CI:
14.3–19.5, p <0.001) compared to other occupational categories. Users were commonly
belonged to nuclear families (n = 151, 12.4%; 95% CI: 10.5–14.2, p = 0.027). Walking was the
preferred transport method (n = 91, 16.9%; 95% CI: 13.7–20.1, p <0.001) and HLC usage was
high when the distance to the HLC was less than 1 km from home (n = 96, 13.6%; 95% CI:
11.1–16.2, p = 0.017) compared to respectively other available transport methods and other
distance categories. Use was high when there was no individual income source (n = 142,
16.4%; 95% CI: 13.9–18.9, p <0.001). Users commonly belonged to the LKR 15,001–30,000
household income category (n = 75, 14.7%; 95% CI: 11.7–17.8, p = 0.015), the second-lowest
household income category compared to other household income categories.
Among the medical and screening history variables, the status of family history regarding
NCDs (p = 0.007) and lifetime experience of undergoing either cholesterol or diabetes screen-
ing (p <0.001) was significantly associated with utilising HLCs. Users commonly had a posi-
tive family history of selected NCDs and risk factors (n = 124, 13.2%; 95% CI: 11.0–15.3,
p = 0.007) and a lifetime experience of either cholesterol or diabetes screening (n = 167, 13.0%,
95% CI: 11.2–14.9, p <0.001).
Utilisation was associated with each of the cognitive and psychological attributes. Users
were aware of the existence or services of HLCs, (n = 195, 26.9%; 95% CI: 23.6–30.1,
p<0.001). Users commonly reported having positive perceptions on susceptibility to NCDs
(n = 175,24.2%;95% CI: 21.1–27.4, p <0.001), positive perceptions on the usefulness of under-
going screening (n = 176, 32.2%; 95% CI: 28.3–36.1, p <0.001), higher mean knowledge (15.6
(SD = 6.26), Median = 17.0 (IQR = 9), Min = 0 Max = 25, p <0.001), higher mean enthusiasm
level on screening (33.5, (SD = 6.51), Median = 35.0 (IQR = 10), Min = 0 Max = 40, p <0.001)
and higher mean enthusiasm on healthy life (19.9, (SD = 9.39), Median = 20.0 (IQR = 20),
Table 1. (Continued)
Characteristic (n = 1727) Frequency %
2.1–3 258 14.9
More than 3.1 193 11.2
The available mode of transport to visit HLC
By public transport 354 20.5
By a rented three-wheeler 182 10.5
By an own vehicle 652 37.8
By walking 539 31.2
https://doi.org/10.1371/journal.pone.0301510.t001
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Table 2. Distribution of male and female clients according to probable factors associated with utilisation of HLCs: Results of bivariate analysis.
Factor Female utilisation Male utilisation Total population utilisation
% (95% CI) p-value % (95% CI) p-value % (95% CI) p-value
Socio-demographic and economic characteristics
Age groups (Yrs)
35–44 16.30 (12.40–20.30) 0.817
#
0.90 (-0.03–2.17) 0.011
#
10.2 (7.71–12.8) 0.532
#
45–54 16.60 (12.73–20.51) 5.90 (2.88–8.93) 12.3 (9.67–14.9)
55–65 15.0 (11.20–18.80) 5.90 (2.88–8.93) 11.2 (8.66–13.8)
Sex
Female - - - - 16.0 (13.74–18.21) <0.001
#
Male - - - - 4.3 (2.81–5.84)
Religion
Buddhism 16.63 (14.23–19.03) 0.090
#
4.35 (2.74–5.96) 0.925
#
11.7 (10.10–13.30) 0.116
#
Non-Buddhist 10.28 (4.43–16.13) 4.11 (0.55–8.77) 7.78 (3.83–11.73)
Marital status
Married 16.35 (14.03–18.66) 0.589
##
4.10 (2.58–5.61) 0.534
##
11.4 (9.90–12.98) 0.802
##
Single 11.54 (-1.62–24.70) 9.38 (-1.30–20.05) 10.3 (2.27–18.42)
Divorced N/A N/A N/A
Widowed 7.14 (-8.29–22.57) N/A 6.3 (7.07–19.57)
Separated N/A N/A N/A
Educational level
No formal education 14.29 (-20.67–49.24) 0.851
#
N/A 0.784
#
10.0 (-0.13–0.33) 0.960
#
Primary 20.45 (8.05–32.86) N/A 12.2 (4.54–19.79)
Low secondary 16.33 (13.37–19.30) 4.31 (2.35–6.26) 11.4 (9.44–13.35)
Upper secondary 15.21 (11.46–18.97) 5.00 (2.10–7.90) 11.3 (8.71–13.90)
Above upper secondary 11.11 (-1.56–23.78) 4.35 (-4.67–13.36) 8.0 (0.21–15.79)
Occupation category
Stay-home mothers 16.88 (14.26–19.50) 0.762
#
N/A 0.097
#
16.9 (14.26–19.50) <0.001
#
Unemployed 22.22 (-11.67–56.12) 10.29 (2.88–17.70) 11.7 (4.35–19.03)
Retired 11.76 (-5.31–28.84) 5.66 (-0.77–12.09) 7.1 (0.96–13.33)
Self-employed 14.61 (7.12–22.09) 2.14 (-0.29–4.57) 7.0 (3.66–10.31)
Daily wager 14.29 (0.47–28.10) 3.77 (1.19–6.36) 5.0 (2.22–7.78)
Non-government 9.09 (0.25–17.93) 2.84 (0.06–5.61) 4.3 (1.37–7.28)
Government 12.07 (3.43–20.71) 6.25 (0.83–11.67) 8.7 (3.94–13.46)
Type of family
Nuclear 17.96 (15.15–20.76) 0.008
#
4.23 (2.46–6.01) 0.855
#
12.4 (10.53–14.23) 0.027
#
Extended 11.33 (7.77–14.88) 4.55 (1.62–7.47) 8.7 (6.22–11.14)
The available mode of transport to travel to HLC
Public transport 13.49 (9.25–17.74) 0.002
#
4.90 (0.64–9.16) 0.454
#
11.0 (7.74–14.29) <0.001
#
Hired three-wheeler 8.53 (3.64–13.41) N/A 6.0 (2.55–9.54)
Own vehicle 14.17 (9.79–18.55) 4.69 (2.62–6.76) 8.3 (6.16–10.40)
Walking 20.99 (17.00–24.97) 4.48 (0.93–8.02) 16.9 (13.71–20.06)
Number of children
0 10.71 (3.96–17.47) 0.160
#
6.85 (0.92–12.78) 0.538
#
8.9 (4.41–13.42) 0.076
#
1–2 15.05 (12.31–17.78) 3.65 (1.94–5.36) 10.3 (8.54–12.10)
3–4 19.78 (15.07–24.50) 5.30 (1.68–8.91) 14.7 (11.32–18.05)
More than 5 15.38 (-7.31–38.08) N/A 11.8 (-5.31–28.84)
Distance from home to nearest health facility (km)
(Continued)
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Table 2. (Continued)
Factor Female utilisation Male utilisation Total population utilisation
% (95% CI) p-value % (95% CI) p-value % (95% CI) p-value
0–1 18.72 (14.98–22.46) 0.053
#
6.03 (3.23–8.82) 0.317
#
13.6 (11.10–16.18) 0.017
#
1.1–2 16.52 (12.62–20.43) 3.17 (0.84–5.49) 11.4 (8.75–13.97)
2.1–3 10.67 (5.67–15.66) 3.70 (0.08–7.32) 7.8 (4.47–11.04)
More than 3.1 10.91 (4.99–16.83) 2.41 (-0.96–5.78) 7.3 (3.56–10.95)
Household income category (LKR per month)
0–15,000 13.24 (9.30–17.19) 0.009
#
5.62 (0.74–10.50) 0.661
#
11.4 (8.20–14.67) 0.015
#
15,001–30,000 21.38 (16.75–26.02) 4.88 (1.90–7.85) 14.7 (11.65–17.82)
30,001–40,000 17.34 (11.64–23.04) 2.27 (-0.30–4.85) 10.8 (7.31–14.33)
40,001–55,000 15.13 (8.59–21.66) 3.23 (-0.43–6.88) 9.9 (5.85–13.96)
55,001–600,000 9.33 (4.62–14.04) 5.14 (1.84–8.45) 7.1 (4.27–9.88)
Medical and screening history
Family history of NCDs or a risk factor
Yes 18.17 (15.04–21.29) 0.027
#
4.82 (2.57–7.06) 0.516
#
13.16 (11.00–15.33) 0.007
#
No 13.06 (9.92–16.21) 3.81 (1.77–5.86) 9.04 (7.03–11.06)
Personnel history of NCDs or a risk factor
No 16.29 (13.97–18.61) 0.248
#
4.27 (2.75–5.80) 0.652
#
11.36 (9.83–12.89) 0.667
#
Yes 10.53 (2.31–18.74) 6.67 (-7.63–20.97) 9.72 (2.71–16.73)
Experience with either diabetes or cholesterol screening
No 6.62 (2.61–10.63) <0.001
#
2.35 (0.05–4.65) 0.213
#
4.36 (2.12–6.61) <0.001
#
Yes 18.28 (15.59–20.98) 4.52 (2.67–6.37) 13.0 (11.2–14.9)
Cognitive and psychological attributes
Perceived susceptibility to NCDs
Negative 3.07 (1.63–4.51) <0.001
#
0.67 (-0.09–1.42) <0.001
#
1.99 (1.13–2.85) <0.001
#
Positive 30.90 (26.74–35.05) 11.11 (7.13–15.09) 24.24 (21.11–27.37)
Perceived usefulness on screening
Negative 1.95 (0.90–3.01) <0.001
#
1.17 (0.24–2.10) <0.001
#
1.61 (0.89–2.33) <0.001
#
Positive 41.42 (36.35–46.48) 13.33 (8.32–18.35) 32.18 (28.25–36.10)
Knowledge on HLCs 15.62
”mean (95%CI)”
(14.65–
16.58)
<0.001
###
15.23
”mean (95%CI)”
(12.89–
17.58)
<0.001
###
15.56
”mean (95%CI)”
(14.68–
16.44)
<0.001
###
Self-assessed health score 68.48
”mean (95%CI)”
(65.59–
71.38)
0.180
###
66.00
”mean (95%CI)”
(60.36–
71.64)
0.007
###
68.10
”mean (95%CI)”
(65.51–
70.69)
0.005
###
Enthusiasm on screening 33.48
”mean (95%CI)”
(32.50–
34.47)
<0.001
###
33.50
”mean (95%CI)”
(30.76–
36.24)
<0.001
###
33.49
”mean (95%CI)”
(32.57–
34.41)
<0.001
###
Enthusiasm for a healthy lifestyle 19.82
”mean (95%CI)”
(18.37–
21.26)
<0.001
###
20.67
”mean (95%CI)”
(17.14–
24.19)
<0.001
###
19.95
”mean (95%CI)”
(18.62–
21.27)
<0.001
###
Family and community characteristics
Perceived presence of peer support
Yes 23.01 (18.68–27.35) <0.001
#
6.10 (2.86–9.34) 0.125
#
16.8 (13.73–19.84) <0.001
#
No 12.13 (9.64–14.61) 3.53 (1.88–5.19) 8.5 (6.91–10.15)
Acceptance of negative gender-related
norms
58.78
”mean (95%CI)”
(56.54–
60.91)
0.08
###
59.83
”mean (95%CI)”
(54.52–
65.15)
0.155
###
58.89
”mean (95%CI)”
(56.89–
60.90)
0.003
###
Acceptance of negative norms on NCDs
and screening
46.27
”mean (95%CI)”
(44.29–
48.25)
0.57
###
48.33
”mean (95%CI)”
(44.37–
52.29)
0.797
###
46.59
”mean (95%CI)”
(44.82–
48.36)
0.981
###
Perceived family support 32.69
”mean (95%CI)”
(31.39–
34.01)
<0.001
###
35.33
”mean (95%CI)”
(31.69–
38.96)
<0.001
###
33.10
”mean (95%CI)”
(31.87–
34.34)
<0.001
###
Perceived community networking 20.15
”mean (95%CI)”
(18.57–
21.73)
<0.001
###
22.00
”mean (95%CI)”
(17.31–
26.69)
0.017
###
20.44
”mean (95%CI)”
(18.93–
21.94)
<0.001
###
Services related perceptions
(Continued)
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Min = 0 Max = 40, p <0.001) than non-users. Users had a lower mean score for self-health
assessment which requested them to give a score to their health from a range of 0 to 100 (68.1
(SD = 18.3), Min = 0 Max = 100, p = 0.005)), than non-users.
Out of the family and community characteristics, all were significantly associated with utili-
sation except “acceptance of negative norms on NCDs and screening” (p = 0.981). Users
reported a lower mean acceptance of negative gender-related norms (58.9 (SD = 14.2),
Median = 60.0 (IQR = 20), Min = 0 Max = 90, p = 0.003)) than non-users.
Among the services-related perceptions, all variables were significantly associated with uti-
lising HLCs except perceived negativity on functioning (p = 0.308). Users mostly reported hav-
ing very high perceived accessibility to HLCs (n = 52, 16.7%, 95% CI: 12.6–20.9, p <0.001)
and a satisfied perception of the quality of the services (n = 68, 95% CI: 12.5–19.5, p <0.001)
compared with other perception categories in both variables.
Multivariable analysis (Table 3). After mutual adjustment for all characteristics catego-
ries, among sociodemographic and economic characteristics, only sex, type of family, distance
to the nearest HLC and household income category were significantly associated with HLC
utilisation. Among cognitive and psychological attributes, the association between perceived
usefulness on screening, perceived susceptibility to NCDs, knowledge on HLCs, self-assessed
health score and enthusiasm on screening with HLC utilisation remained after multivariable
adjustment while enthusiasm for a healthy life did not show an association. Under family and
community factors, perceived family support, perceived community networking and perceived
peer support remained as predictors of HLC utilisation while acceptance of negative gender-
related norms was not related to HLC utilisation after multiple adjustments. Under services-
related perceptions, perceived accessibility and perceived quality of services remained associ-
ated with HLC utilisation. Medical and screening history variables did not predict utilisation
behaviour in multivariable analysis.
Table 2. (Continued)
Factor Female utilisation Male utilisation Total population utilisation
% (95% CI) p-value % (95% CI) p-value % (95% CI) p-value
Perceived accessibility to HLCs
Very high 19.44 (14.52–24.36) 0.033
#
5.08 (-0.69–10.86) 0.001
#
16.7 (12.55–20.89) <0.001
#
High 11.22 (7.70–14.74) 11.76 (3.91–19.62) 11.3 (8.12–14.52)
Moderate 19.55 (14.75–24.35) 6.09 (2.97–9.20) 13.3 (10.31–16.31)
Low 14.36 (9.30–19.42) 1.59 (0.2–2.98) 6.4 (4.23–8.52)
Very low 13.33 (-6.15–32.82) N/A 5.3 (-0.0217–0.1270)
Perceived quality of services
Totally-unsatisfied 8.40 (3.58–13.21) <0.001
#
3.37 (-0.45–7.19) 0.317
#
6.4 (3.11–9.61) <0.001
#
Unsatisfied 9.89 (6.33–13.45) 2.75 (0.35–5.14) 7.0 (4.67–9.39)
Neutral 19.18 (15.12–23.24) 4.07 (1.58–6.55) 13.1 (10.41–15.78)
Satisfied 22.05 (16.91–27.18) 7.06 (3.17–10.95) 16.0 (12.53–19.54
Totally satisfied 10.00 (-12.62–32.62) N/A 5.9 (-0.0659–0.1835
Perceived negativity on functioning 25.82
”mean (95%CI)”
(24.57–
27.07)
0.204
###
24.33
”mean (95%CI)”
(20.58–
28.09)
0.560
###
25.59”
mean (95%CI)”
(24.40–
26.78)
0.308
###
#
Chi square test,
##
Fishers exact test,
###
Mann Whitney U test, Significance level p<0.05.
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Table 3. Results of logistic regression analysis using utilisation status as the dependent variable for statically significant factors in bivariate analyses.
Variable Female Male Total population
AOR (95% CI) p value AOR (95% CI) p value AOR (95% CI) p value
Socio-demographic and economic factors
Sex
Female*- - - - - 0.002
Male - - - - 0.18 (0.05–0.52)
Occupation category
Stay-home mother*- 0.300 - - - 0.412
Unemployed ** 0.88 (0.02–33.27) 0.943 - 0.470 3.29 (0.54–20.28) 0.199
Retired 0.03 (0.00–1.45) 0.076 0.001 (0.00–0.88) 0.046 0.29 (0.04–2.47) 0.262
Self-employed 1.04 (0.23–4.78) 0.962 0.000 (0.00–35.33) 0.148 0.85 (0.24–3.05) 0.805
Daily wager 3.45 (0.51–23.54) 0.206 0.08 (0.00–51.66) 0.446 1.98 (0.50-) 0.326
Non-government 0.17 (0.02–1.74) 0.136 0.05 (0.00–56.71) 0.398 0.47 (0.10–2.19) 0.340
Government 1.11 (0.19–6.47) 0.911 0.00 (0.00–5.18) 0.097 1.09 (0.28–4.27) 0.900
Type of family
Nuclear *- 0.035 - 0.644 - 0.021
Extended 0.39 (0.16–0.94) 0.50 (0.03–9.37) 0.43 (0.21–0.88)
The available mode of transport to visit HLC
Public transport*- 0.971 - 0.632 - 0.821
Hired three-wheeler 0.69 (0.15–3.13) 0.631 0.00 (0.00) 0.998 0.52 (0.14–2.03) 0.349
Own vehicle 0.91 (0.30–2.76) 0.870 52.99 (0.12–26716.96) 0.211 0.87 (0.34–2.19) 0.761
By walking 0.93 (0.33–2.67) 0.896 46.50 (0.02–89748.38) 0.320 0.79 (0.31–1.99) 0.610
Distance to nearest health facility (km)
0–1*- 0.025 - 0.253 - 0.004
1.1–2 0.31 (0.12–0.77) 0.011 0.01 (0.00–1.18) 0.058 0.29 (0.14–0.63) 0.002
2.1–3 0.59 (0.14–2.56) 0.480 0.02 (0.00–13.27) 0.241 0.46 (0.15–1.39) 0.169
More than 3.1 0.14 (0.03–0.61) 0.009 0.00 (0.00) 0.110 0.14 (0.04–0.53) 0.004
Household income category (LKR per month)
0–15,000*- 0.033 - 0.346 - 0.013
15,001–30,000 3.92 (1.41–10.93) 0.009 340.69 (0.44–262174.87) 0.085 3.69 (1.53–8.89) 0.004
30,001–40,000 3.29 (1.04–10.37) 0.042 26.64 (0.03–23994.07) 0.344 2.84 (1.02–7.94) 0.046
40,001–55,000 1.74 (0.45–6.66) 0.420 0.02 (0.00–157.29) 0.380 1.15 (0.37–3.63) 0.807
55,001–600,000 0.68 (0.17–2.81) 0.595 114.20 (0.02–821508.06) 0.296 0.99 (0.30–3.24) 0.987
Medical and screening history
NCD family history
No*- 0.562 - 0.067 - 0.164
yes 0.79 (0.37–1.71) 0.012 (0.00–1.37) 0.63 (0.33–1.21)
Experience with either blood sugar or cholesterol screening
No *- 0.269 - 0.671 - 0.113
Yes 0.39 (0.08–2.05) 0.37 (0.00–34.37) 0.366 (0.11–1.27)
Cognitive and psychological attributes
Perceived usefulness on screening
Negative perception*-<0.001 - 0.029 - <0.001
Positive perception 14.89 (5.43–40.81) 933.52 (2.01–434253.29) 10.15 (4.40–23.38)
Knowledge on HLCs 1.32 (1.24–1.41) <0.001 2.70 (1.315.56) 0.007 1.31 (1.24–1.38) <0.001
Self-assessed health score 0.97 (0.95–0.99) 0.015 0.95 (0.86–1.05) 0.335 0.97 (0.95–0.99) 0.001
Perceived susceptibility to NCDs
Negative perception*---
(Continued)
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As there was a significant difference between males and females, a comprehensive subgroup
analysis was done to understand the specific factors influencing the underutilization among
the two gender groups.
In females, belonging to an extended family (OR = 0.39 (95% CI: 0.16–0.94, p = 0.035) was
associated with decreased odds of utilising HLCs compared with those belonging to a nuclear
family. Females had 0.31 (95% CI: 0.12–0.77, p = 0.011) and 0.14 (95% CI: 0.03–0.61,
p = 0.009) times lower odds of utilising HLCs if they resided 1-2km and more than 3km dis-
tance categories compared with females resided in less than 1km distance. Females from the
second (15,001–30,000 LKR) and third (30,001–40,000 LKR) lowest household income catego-
ries had a 3.92 (95% CI: 1.41–10.93, p = 0.009) and 3.29 (95% CI: 1.04–10.37, p = 0.042) times
higher odds of become a user compared with females belong to the lowest household income
category (<15,000 LKR).
Among cognitive and psychological attributes, positive perceptions on the usefulness of
screening (OR = 14.89 (95% CI: 5.43–40.8), p <0.001), positive perceptions on susceptibility
to NCDs (OR = 5.03 (95% CI: 1.72–14.7), p = 0.003), increment in knowledge (OR = 1.32
(95% CI: 1.24–1.41), p <0.001), and increment in enthusiasm on screening (OR = 1.10 (95%
Table 3. (Continued)
Variable Female Male Total population
AOR (95% CI) p value AOR (95% CI) p value AOR (95% CI) p value
Positive perception 5.03 (1.72–14.65) 0.003 310.95 (3.81–25368.32) 0.011 6.78 (2.79–16.42) <0.001
Enthusiasm on screening 1.10 (1.044–1.17) 0.002 1.144 (0.92–1.39) 0.230 1.09 (1.04–1.14) <0.001
Enthusiasm for a healthy lifestyle 1.01 (0.97–1.06) 0.518 0.93 (0.79–1.11) 0.428 0.99 (0.97–1.03) 0.959
Family and community characteristics
Acceptance of negative norms related to gender 1.03 (1.00–1.06) 0.040 1.06 (0.95–1.19) 0.316 1.02 (0.99–1.04) 0.064
Perceived family support 1.15 (1.10–1.21) <0.001 1.35 (1.047–1.727) 0.020 1.14 (1.09–1.19) <0.001
Perceived community networking 1.11 (1.06–1.16) <0.001 1.09 (0.92–1.29) 0.343 1.08 (1.04–1.12) <0.001
Perceived presence of peer support
Yes
No 5.79 (2.36–14.17) <0.001 0.044 (0.00–2.03) 0.108 3.12 (1.53–6.34) 0.002
Services related perceptions
Perceived accessibility to HLCs
Very high *0.001 0.211 0.001
High 0.28 (0.09–0.85) 0.025 223.11 (0.24–210360.59) 0.122 0.44 (0.17–1.16) 0.096
Moderate 1.12 (0.37–3.38) 0.848 1.65 (0.00–838.61) 0.876 0.96 (0.37–2.48) 0.926
Low 0.10 (0.03–0.39) 0.001 0.09 (0.00–17.65) 0.368 0.12 (0.04–0.36) <0.001
Very low 2.39 (0.12–48.49) 0.569 0.00 (0.00) 0.997 0.50 (0.04–5.744) 0.581
Perceived quality of services
Totally-unsatisfied*0.012 0.698 0.015
Unsatisfied 0.734 (0.13–4.10) 0.727 0.17 (0.00–154.17) 0.614 0.59 (0.15–2.36) 0.460
Neutral 3.52 (0.70–17.58) 0.126 10.89 (0.04–3383.15) 0.415 2.27 (0.66–7.799) 0.192
Satisfied 3.37 (0.65–17.47) 0.148 0.67 (0.01–86.31) 0.874 2.12 (0.61–7.45) 0.240
Totally satisfied 0.06 (0.00–3.59) 0.176 0.00 (0.00) 0.999 0.03 (0.00–2.60) 0.124
Constant 0.00 <0.001 0.00 0.032 0.00 <0.001
*Reference category
**Reference category of males for occupation category.
AOR: Adjusted Odds Ratio.
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CI: 1.04–1.17), p = 0.002) presented as predictors of HLC utilisation. An increment in the
self-health score (OR = 0.97 (95% CI: 0.95–0.99), p = 0.015) was related to lower utilisation.
Under family and community factors, female users were found to be increased by 1.03 (95%
CI: 1.00–1.06, p = 0.040) times with a unit increment in acceptance on norms related to gen-
der, 1.15 (95% CI: 1.10–1.21, p <0.001) times with a unit increment in perceived family sup-
port, 1.11 (95% CI: 1.06–1.16, p <0.001) times with a unit increment in perceived
community networking. Females with perceived peer support had 5.79 (95% CI: 2.36–14.17,
p<0.001) times higher odds of becoming a HLC user compared with their counterparts.
Under services-related perceptions, females who perceived accessibility to HLC as high or
low respectively had a 0.28 (95% CI: 0.09–0.85, p = 0.025) and 0.10 (95% CI: 0.03–0.39,
p = 0.001) times lower odds of become a user compared with females who perceived accessi-
bility to HLC is very high.
In males, the retired occupation category was associated with a decreased odds of
(OR = 0.001 (95% CI: 0.00–0.88), p = 0.046) utilising HLCs compared with unemployed
males. Positive perceptions on the usefulness on screening (OR = 310.95 (95% CI: 3.81–
25368.32), p = 0.029), positive perceptions on susceptibility to NCDs (OR = 933.52 (95% CI:
2.01–434253.29), p = 0.011), increment in knowledge on HLCs (OR = 2.70 (95% CI: 1.31–
5.56), p = 0.007) and increment in perceived family support (OR = 1.35 (95% CI: 1.05–1.73),
p = 0.020) were the only other predictors of male HLC utilisation.
Discussion
Summary of findings
This study aimed to describe the underutilisation of HLCs and determine the factors associ-
ated with the utilisation of HLCs in Sri Lanka. The awareness about the existence of HLCs in
the study sample was 42% (n = 726, 95% CI: 39.7–44.4). The prevalence of utilising HLCs was
11.3% (n = 195, 95% CI: 9.80–12.8) and females (n = 165; 16.0%; 95% CI: 13.74–18.21) had a
higher utilisation compared to males (n = 30; 4.3%; 95% CI: 2.81–5.84). After multivariable
analysis, 14 factors were significantly associated with the utilisation of HLCs. The most influ-
ential factor was having a positive perception on the usefulness of screening (OR = 10.2, 95%
CI: 4.04–23.4). In addition, the odds of utilising HLCs had increased by respectively 6.78 (95%
CI: 2.79–16.42) and 3.12 (95% CI: 1.54–6.34) times if a respondent thought s/he is susceptible
to NCDs and if s/he perceived presence of peer support for screening and a healthy lifestyle.
Other significant predictors that improve HLC utilisation were the second and third lowest
household income categories of 15,001–30,000 LKR (OR = 3.69, 95% CI: 1.53–8.89) and
30,001–40,000 LKR (OR = 2.84, 95% CI: 1.02–7.94), higher level of knowledge on HLCs
(OR = 1.31, 95% CI: 1.24–1.38), higher enthusiasm on screening (OR = 1.09, 95% CI: 1.04–
1.14), higher perceived family support (OR = 1.14, 95% CI: 1.09–1.19) and higher perceived
community networking (OR = 1.08, 95% CI: 1.04–1.12). Male sex (OR = 0.18, 95% CI: 0.05–
0.52), belonging to an extended family (OR = 0.43, 95% CI: 0.21–0.88), resided within 1–2 km
(OR = 0.29, 95% CI: 0.14–0.63) or more than 3 km (OR = 0.14, 95% CI: 0.04–0.53) of the HLC,
having a higher self-assessed health score (OR = 0.97, 95% CI: 0.95–0.99) and low perceived
accessibility to HLCs (OR = 0.12, 95% CI: 0.04–0.36) were significantly reduced the HLC utili-
sation. Similarities between males and females were observed with regard to the type of percep-
tion on susceptibility to NCDs and the usefulness on screening, knowledge on HLCs, and
perceived family support. The first two strong positive predictors of HLC utilisation among
both genders were positive perceptions on the usefulness on screening and positive percep-
tions on susceptibility to NCDs.
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Implications and comparison with other study findings
Our findings align with previous global studies showing that males were less likely to undergo
screening than females [16,17,21,22]. The female-to-male utilisation ratio of the current study
was 5.5:1. This disproportionate ratio is higher than the country rates in 2018 (2.2:1) and 2019
(2.6:1)[23]. Reporting a high percentage of female users is compatible with the consecutive
national reports for the past ten years [9]. We found that this is due to relatively high health-
seeking behaviour among females compared to males, which was also consistent with the
global literature [16,17]. The commonest reason that influences health health-seeking behav-
iour of the males was masculine perceptions [24–27], which was also consistent with our prior
qualitative study done with the same study population [20]. Our qualitative findings indicated
that gender stereotyping defines boundaries for men not to opt for healthy choices common to
both males and females. This might be due to social stigma and negative peer influence associ-
ated with deviation from gender norms [24]. Thus, males were unprepared to utilise screening
and lifestyle modification services compared to females, a well-established risk factor for
increased NCD risk, leading to increased premature mortality and reduced life expectancy
among them [16]. Previous literature indicates that the opportunity to participate without gen-
der bias is a key measure of the success of community screening programmes [28]. Thus, cur-
rent study findings imply the need for more effective promotional campaigns such as social
marketing, social media, or mass media targeted at males that address all the relevant norms
and modifications to the current services to accommodate male clients [29].
We have found that users were commonly from the second lowest household income cate-
gory. Reporting a greater number of users with a lower household income is consistent with
previous studies in developing countries namely India [30], Malaysia [31], and Nigeria [32]. In
contrast to this, users in developed countries were commonly from higher income categories
as evidenced by previous systematic reviews [16,17]. However, our study has also found that
there was an access issue for the first household income category people (lowest level of
income) to the HLC because the least number of users were reported from this category com-
pared with other categories. According to our previous qualitative study on the same topic and
the population, the daily wagers do not give up their daily income just participate in a screen-
ing session conducted on a weekday morning, especially when they don’t have any symptoms
[20]. Even though, multivariable analysis have confirmed that household income category is a
significant predictor for total population and females. Our findings implied that HLCs are
serving impoverished groups by being affordable to them. Thus, in a way, our study provides
evidence that the service aim of the Ministry of Health is achieved with some lowest levels of
household income groups. However, at the same time our study also provides evidence for the
fact that the service should be reorganized to cater for the lowest-income group of Sri Lanka.
Being a member of the nuclear family and living less than one kilometre from the HLC
were significant predictors of female HLC utilisation, indicating that if immediate social and
physical access-related barriers are overcome, they will utilise the HLC. However, none of the
sociodemographic and economic characteristics was significantly associated with males except
being retired from the occupation. This can be interpreted as evidence that occupation is a
main barrier to utilisation for males, even when other barriers are absent [16]. This strongly
highlights the need to change the service delivery structure, if to improve male participation.
None of the medical and disease history factors was significantly associated with HLC utili-
sation among both males and females in the multivariable analysis. It implies that the factors
that were positive in the bivariate analysis were positive due to the effect of confounding by
other variables that were included in the multivariable analysis. As a systematic review of
global literature also reports them as neutral factors for self-motivation for screening, one may
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decide to ignore their importance [16]. However, as some of the primary studies have reported
them as facilitators and persons with a family or medical history have a well-proven increased
risk for NCDs, it is advisable not to ignore this factor in planning interventions and promotion
strategies related to HLC utilisation [33].
We found having positive perceptions on susceptibility to NCDs and the usefulness on
screening were the most influential predictors of HLC utilisation by both genders. Psychologi-
cal models such as the health belief model, theory of reasoned action, and health action process
approach also demonstrate how proactive perceptions on susceptibility guide individuals to
change behaviour [34]. However, there was inconsistent evidence on perceived susceptibility
to NCDs as a predictor of screening utilisation. Some literature showed higher perceived sus-
ceptibility as a more robust predictor [34] while other findings did not support that claim [35–
37]. However, one intervention conducted in the United States reported that individuals uti-
lised screening services more after their risk perception improved [38]. Thus, these findings
highlight that the target population will not utilise HLCs if screening-specific perceptions are
not positive.So, it is important to aim at developing positive perceptions without merely
improving knowledge in future HLC promotions. This fact was highlighted in the global litera-
ture as well [2,5]. According to the sex-specific analysis, males had a higher chance of utilising
HLCs if they had a positive perception of susceptibility to NCDs and the usefulness on screen-
ing, knowledge on HLCs, and enthusiasm on screening than females. These findings highlight
important implications for Sri Lankan policymakers on HLC to design male-specific strategies
to improve HLC utilisation among them. Based on our findings, such male-specific strategies
should be designed to improve positive perceptions on susceptibility to NCDs and usefulness
on screening, knowledge on HLCs and enthusiasm on screening and can range from male-
sensitive educational interventions and video-based educational interventions to partner edu-
cational interventions [29,39].
A previous study conducted in Germany reports that males had higher odds of utilising
screening services with high or intermediate social support compared to females [3]. In con-
trast, we found that the perceived presence of peer support was only associated with the total
population and females. Global literature showed that social networks [40], social support
[41], and neighbourhood social cohesion [42] as vital determinants of better health and health
behaviour. These factors are conceptually similar to perceived community networking in our
study which showed a higher HLC utilisation by individuals with higher perceived community
networking than their counterparts. In the sex-specific analysis, this factor was also signifi-
cantly associated with female HLC utilisation. These findings imply that families, peers and
communities can play significant roles in improving attendance to screening programmes
within communities as indicated by the two action areas in the Ottawa Charter for health pro-
motion: Strengthening community action and developing a supportive environment for health
[43]. Under strengthening community action, health professionals can empower different
community segments to conduct the situational analysis of HLC utilisation, design and imple-
ment collective actions to address determinants of HLC utilisation within their communities
and monitor the progress. Developing a supportive social environment is important to
enlighten the community about the importance of screening, improve positive perceptions of
susceptibility of NCDs, share knowledge about HLCs, provide peer support and for commu-
nity networking. A supportive physical environment such as timeslots within existing commu-
nity-based organisations is essential to provide avenues for sharing knowledge about HLCs
and to improve enthusiasm on screening. These avenues will also benefit from improving peer
support and community networking for HLC utilisation [20,43]. Thus, the above findings
highlight a need to consider these factors apart from cognitive and psychological factors when
developing interventions to improve HLC utilisation.
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Previous studies highlighted that low screening utilisation was due to the masculine views
of men [3,24]. Nevertheless, we only observed an increased odds of utilising HLCs by females
with a unit increment in acceptance of negative gender norms. Social norms are a factor con-
sidered important in behaviour change theories, such as the theory of planned behaviour and
the theory of reasoned action [34]. However, a meta-analysis showed that to date, subjective
norms possessed only a medium-sized relationship to participating in screening [44]. In our
study, acceptance of negative norms on NCDs and screening was not found to be associated
with utilisation. This can be because there was a high degree of acceptance for these norms
irrespective of the utilisation status among the study participants.
Perceived accessibility and quality of the services were significant predictors of HLC utilisa-
tion which was also reported by another local study [45]. Global literature showed that
improving accessibility via flexible appointment times and conducting screening after office
hours and on weekends would probably increase public participation, which is also applicable
to the HLCs [46]. Our findings imply the need to improve the accessibility and quality of the
services parallel to the increasing demand for HLCs. However, the implementation of such
measures will be challenged by the availability of human resources and health financing issues
in low and middle-income countries [35]. Literature shows that community-based interven-
tion could improve public screening participation in low and middle-income countries [47].
Therefore, power can be delegated to local communities to promote the HLC, through
improving family and peer support, and community networking. For this purpose, innovative
community-based promotions that are designed on identified predictors can be used to mar-
ket unique services like health education sessions, lifestyle modification sessions, and follow-
ups.
Strengthens and limitations
There were several strengths of this study. (1) first community-based study about potential fac-
tors associated with HLC utilisation in Sri Lanka (2) testing factors related to five-variable cate-
gories exclusively identified by a prior qualitative study (3) logistic regression model to
account for possible confounders of HLC utilisation. The external validity was ensured by
obtaining a representative sample using an appropriate probability sampling method. The,
sample size was decided using a standard formula to ensure an adequate sample size. Adequate
coverage of the study sample was achieved by using measures to reduce non-response. Non-
response was avoided by training enumerators to effectively explain the study’s general objec-
tive and importance. In the absence of inhabitants in the selected households, the houses were
visited at least three times before classifying them as “non-respondent.” Thus, results can be
generalized to the study population.
However, our study design was cross-sectional and thus could not establish causal relation-
ships like in prospective studies. Measures were taken to account for identified bias to ensure
internal validity of data. As the data were collected through an IAQ, interviewer bias could
have occurred. Therefore, enumerators were trained on building rapport with the interview-
ees, not showing judgmental reactions to the responses, and not giving cues on expected
answers. There could be recall bias for HLC clients because certain questions were asked about
the client’s perceptions or experiences before they visited the HLC. Therefore, the reported
answers for perceived susceptibility to NCDs, perceived usefulness of screening, enthusiasm
on screening and enthusiasm on healthy lifestyle could be overestimated responses. HLC cli-
ent’s could report a higher knowledge about HLCs due to their visits to the HLC. However, it
was assumed that this effect is trivial as improving the knowledge on HLCs is not targeted in
the health education session. There was a possibility of receiving social desirability answers for
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items assessing perceived family support, community networking and peer support and ser-
vices related perceptions prior to their HLC visit. It was assumed that there might be no or
minimal effect from HLC visit on these variables for clients as there is no systematic involve-
ment to change these variables from HLC. However, enumerators were trained mainly on the
method of administering all above types of questions to minimize bias. Moreover, there can be
response bias in getting the answer for the household income, because we used a direct answer
question for that variable. It is a limitation of our study. There were statistical concerns as the
actual strength of the association between tested factors and male utilisation was inconclusive
due to the large standard error in odds ratios caused by the lower number of male users.
Conclusion and recommendations
Awareness and utilisation of the freely available HLCs are low among the target population in
Sri Lanka. Multiple factors operating at different levels accounted for the underutilisation of
HLCs. Therefore, HLC utilisation would not be improved by only improving knowledge on
HLCs, because of the influence of personal attitudes along with wide community factors
namely family support, community networking and peer support. Thus, interventions aiming
to improve HLC utilisation should be complex and multifaceted designs addressing these fac-
tors. Moreover, according to the bivariate and multivariable analysis these predictors of HLC
utilisation differed with gender, with men showing low utilisation. Hence, gender-sensitive
innovative interventions will potentially improve HLC utilisation, including specific strategies
focusing on men.
Supporting information
S1 Data set.
(SAV)
S1 Questionnaire.
(DOCX)
Author Contributions
Conceptualization: Thilini Herath.
Data curation: Thilini Herath.
Formal analysis: Thilini Herath.
Investigation: Thilini Herath.
Methodology: Thilini Herath.
Project administration: Thilini Herath.
Supervision: Manuja Perera, Anuradhani Kasturiratne.
Writing – original draft: Thilini Herath.
Writing – review & editing: Manuja Perera, Anuradhani Kasturiratne.
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