Insights into the morbidity profiles of epidemiologically excluded COVID-19 patients in primary care settings during the third wave of the pandemic in the Anuradhapura District, Sri Lanka

Abstract

Background

The COVID-19 pandemic has dramatically impacted healthcare systems worldwide, leading to changes in the delivery of healthcare services. A profound effect on the well-being of non-COVID-19 patients has been reported, but limited evidence is available from developing countries. This study aimed to describe the morbidity profiles of epidemiologically excluded COVID-19 patients during the pandemic in the primary care setting of the Anuradhapura District of Sri Lanka.

Methods

This cross-sectional healthcare institution-based study collected morbidity profiles from six state-owned and five private primary care facilities (PCFs) in the Anuradhapura District during the third wave of the COVID-19 pandemic. Reasons for Encounters (RFEs) were recorded from physically available and epidemiologically excluded COVID-19 patients in a paper-based data format and coded using the International Classification of Primary Care.

Results

Out of 1630 primary care encounters, 187 RFEs were identified. Most patients were females (n = 899, 55%) and in the adult age category (n = 1297, 79%). The median age of the patients was 39 years (interquartile range: 21–55). Older patients were likelier to seek primary care in the state sector (p < .001). Most children presented to the private sector compared to state PCFs (p < 0.001). The majority of females significantly utilised state sector PCFs (p = 0.043). Upper respiratory tract infections (n = 154, 9.00%) were the most common RFE. The highest burden of systemic RFEs was associated with dermatological (n = 294, 18%) and respiratory conditions (n = 274, 16%). More than one-third of adults (n = 487, 37.5%) suffered from a self-reported non-communicable disease (NCD). Hypertension (n = 235, 48%), diabetes mellitus (n = 184, 38%), and dyslipidemia (n = 134, 28%) were the most observed NCDs. Multimorbidity was reported in 195 (40%) adult patients with an NCD.

Conclusion

The pandemic has led to a shift in primary care morbidity profiles, with a higher incidence of dermatological and respiratory diseases and NCDs among healthcare seekers. Patients sought care from the state and private sector differently depending on age, sex, and nature of illness. Primary care services must prepare to adapt to changes in healthcare-seeking patterns and morbidity profiles during pandemics to ensure comprehensive care is available on demand.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-025-02792-3.

Introduction

The coronavirus disease (COVID-19) pandemic has emerged as one of the most significant global health challenges of the twenty-first century, impacting healthy lifestyles and transforming healthcare systems worldwide. After the first case of COVID-19 was detected in December 2019 in Wuhan, China, the WHO declared a Public Health Emergency of International Concern (PHEIC) on January 30 2020 [1]. The PHEIC was declared over by May 05 2023, and during this period, COVID-19 spread globally with varied complexity, taking the lives of millions. As a prompt response, the primary care system underwent promising structural and strategic changes in service provisions to fully commit to all disease prevention activities related to COVID-19 [2]. While there was an overwhelming demand for the prevention of COVID-19 in the health sector, non-COVID-19 medical illnesses also contributed to significant morbidity. The latter was primarily underreported in health system research, and there is insufficient evidence to support comparisons during the pandemic.

Pre-pandemic morbidity due to non-communicable diseases (NCDs) was significant because they accounted for 44% of global deaths, above all other medical conditions [3]. Ischaemic heart diseases and cancer were the leading causes of death globally and regionally in high and upper-middle-income Asia–Pacific countries [3, 4]. Psychiatric illnesses have resulted in noteworthy mortality, and they were among the top ten causes of global deaths in 2019 [3]. According to a survey conducted in the United States, septicemia, cardiac conditions (heart failure, myocardial infarction, cardiac arrhythmias), and renal failure were the most common causes of hospital admissions [5]. However, among Asian countries, accidents, poisoning and violence, infections of the skin and other subcutaneous tissues, intestinal infectious diseases and disorders in the gall bladder, biliary tract and pancreas were associated with higher hospital morbidity [6]. A systemic review conducted in 12 countries across five continents revealed that upper respiratory infections and hypertension were among the most frequent clinician-reported primary care encounters (PCE), and cough, back pain, and abdominal symptoms were the most patient-reported conditions [7].

With the pandemic, the primary care settings observed a considerable reduction in patients diagnosed with cardiovascular risk factors, chronic disease conditions, and several malignancies [8, 9]. The trend of deaths by communicable diseases was augmented with COVID-19-associated respiratory tract infections by the end of 2021 [10]. During the pandemic Alzheimer's and other dementias were a leading cause of death among United States citizens [11]; however a less burden of mental health-related issues has been reported in other countries [9, 12]. The main reasons for Canadian hospital admissions in 2021 and 2022 were childbirth, COVID-19, heart failure, heart attack and substance use disorders [13]. In the United Kingdom, the weekly reported incidents of asthma, intestinal infectious diseases, and acute respiratory tract infections were significantly reduced during the COVID-19 pandemic [14]. Routine follow-up visits were substantially reduced for patients with renal, cardiac and neurologic problems and patients obtaining antiretroviral treatments [15].

The changes in the morbidity patterns observed in the pandemic compared to the pre-pandemic period led to a transformation in healthcare delivery and utilisation patterns among healthcare seekers [16, 17]. A study in a rural district of India reported that the prevalence of COVID‑19‑induced healthcare facility avoidance was 15.5% [18]. Patients' negative perceptions of healthcare systems being overstretched, fear of COVID-19 exposure in public places, and directives to self-isolate kept more patients at home, preventing them from seeking care from hospitals [17]. While there is an extensive research on COVID-19-related morbidity and mortality in Asian countries, data on  non-COVID-19 patients in primary care is insufficient for evaluating morbidity patterns.

Sri Lanka is a low-middle-income country with better maternal and child health indicators [19] and universal health coverage than other South Asian countries [20]. There have been a minimum number of pandemic disasters encountered by Sri Lankans before the COVID-19 pandemic, e.g. dengue, measles, and leptospirosis [21]. The mortality patterns of NCDs in pre-pandemic had been similar to global trends and were mainly caused by ischaemic heart disease, malignancies and cerebrovascular conditions [19]. The most reported pre-pandemic reasons for hospital admissions in the country were traumatic injuries, abnormal clinical and laboratory findings, diseases of the urinary and gastrointestinal systems and obstetric conditions [19]. The most clinician-reported reasons for primary care visits in state and private sectors were respiratory tract infections and fever [22], and patient-reported reasons were body aches and pains, cough and cold, and abdominal pain [23]. With the COVID-19 outbreak, essential services for endemic diseases such as dengue were reported to be compromised and insufficient in Sri Lanka [24]. The annual 58 million state-sector outpatient consultations [25] were reduced by 34% during the pandemic [19]. However, little is known about the specific impact of the pandemic on the primary care morbidity profiles in Sri Lanka. This study aimed to explore the morbidity profiles of epidemiologically excluded COVID-19 patients attending primary care facilities (PCFs) in the Anuradhapura district of Sri Lanka during the third wave of the COVID-19 outbreak.

Methods

Study design and setting

This PCF-based cross-sectional study was conducted in the Anuradhapura District of Sri Lanka from October 2021 to January 2022. The third wave of COVID-19 in Sri Lanka lasted from April to December 2021. Since September 2021, the epi curve of COVID-19 has flattened in Sri Lanka [26]. The data collection started after the lockdown when public travel and transport regulations were less-restricted in the country.

The Anuradhapura district is the largest of all 25 districts in Sri Lanka, with nearly 1 000 000 people, of which approximately 94% live in rural. The majority of the population depends on the allopathic system for healthcare needs. Both the state and private sectors provide primary care services. The allopathic sector PCFs included 21 primary medical care units (PMCUs), 33 divisional hospitals (DHs) and, six outpatient departments (OPDs) of base hospitals (BHs) and the teaching hospital (TH). Although PMCUs and DHs lack most investigations except for few blood and urine tests, the OPDs of secondary and tertiary hospitals perform these tests on demand within the premises free of charge. Approximately two million outpatient attendance was reported in the district by 2020, and BHs and DHs recorded the highest number of visits [19].

The private sector mainly consists of private hospitals with OPDs and general practices (GPs), but data on the exact number of private sector PCFs is unavailable. Private hospitals with OPDs typically provide paid diagnostic investigations, which include most of of haematological, biochemical, pathological, and radiological tests. Some of the vaccines in Sri Lanka's ‘Expanded Program on Immunisation’ are available in the private sector. GPs are small-scale PCFs led by a medical officer with fewer facilities for haematological and biochemical investigations, which private laboratories mediate.

Study population and sample selection

The sample size was calculated to identify an illness presenting to the PCF with a minimum prevalence of 4% and an absolute precision error of 0.01%. The estimated minimum effective sample size was 1475. The state and private sector samples were divided in a 4:3 ratio, reflecting the proportion of Sri Lankan households utilising either a state or private PCF within the previous month [27]. Accordingly, the minimum morbidity profiles expected from state and private sector PCFs were 842 and 632. The sample size of each state PCF at the primary, secondary, and tertiary levels was calculated by the probability proportionate to previous year's outpatient attendance at each level of care. The researchers decided on the number of PCFs to meet the estimated sample size at different levels of care, and five state PCFs were selected. A consecutive sample was selected until the expected sample sizes allocated for each PCF were reached. At the primary level of care, two DHs (Rambewa, Parasangaswewa) and a PMCU (Puliyankulama); at the secondary level of care, two OPDs of BHs (Medawachchiya, Padaviya) and at the tertiary level of care, the OPD of the TH of Anuradhapura were included in the study.

Due to the unavailability of a proper database on private sector GPs, selection of private PCFs was based on a convenient sampling method. The study included GPs with more than ten daily PCEs and those located in different residential areas. The five GPs were from ‘Saliya Mawaththa’, ‘Saliyapura’ ‘Mihinthale’, ‘Puvarasankulama’ and ‘Nochchiyagama’.

All patients attended to state OPDs from 8.00 am to 4.00 pm on weekdays, and GPs from 4.00 pm to 8.00 pm daily were selected for the study. The patients were eligible for the data collection if they were physically available at the PCF. Epidemiologically, COVID-19 was excluded before outpatient consultation by evaluating records of patient self-triage and healthcare worker-mediated triage, per guidelines issued by the Ministry of Health Sri Lanka (MoH). Next, the patients highly suspicious of COVID-19 were referred to receiving centres in state PCFs [28]. The diagnostic tests (rapid antigen test or RAT) were not used outpatient to confirm COVID-19 except for specific criteria given by the MoH [29]; thus, confirmation of COVID-19 status except for epidemiological criteria was challenging.

Study instruments and data collection

The development of the study instrument ‘The Patient Morbidity Profile Collector’ (PMPC) (Additional file 1) originated from the primary care coding system introduced by the WONCA (World Association of Family Doctors), 'The International Classification of Primary Care (ICPC)’ [30]. ICPC is divided into 17 chapters by body systems to recognise the reason for encounters (RFEs) based on the localisation and nature of the problem or disease. It also includes a chapter on recognising PCEs for general processes in primary care, such as health consultation, counselling, and wound care. The paper-based PMPC recorded socio-demographic details of patients such as patients' age, sex, occupation, type of residency and civil status. The disease-related data recorded were the presenting complaint or RFE, duration of symptoms, associated symptoms, limitations to daily activities, past medical history, examination findings, most probable diagnosis of the doctor, ICPC code, nature of encounter and management process. Chronic diseases were identified if any self-reported medical conditions existed for more than one year and if the patients were ongoing medical follow-ups.

The trained MBBS graduates completed the PMPC after obtaining informed written consent. Demographic information was collected while patients waited for their consultation, and RFEs were gathered during the consultation.

Data analysis

IBM SPSS statistics (Statistical Package for the Social Sciences), version 24, was used for the analysis. Missing data within the dependent variables were excluded at the first data analysis stage. The descriptive statistics were performed to describe sample characteristics and RFEs based on systemic complaint under each ICPC chapter. The association of patients’ age and sex with the choice of sector of PCF was analysed using the chi-square test. The significance of the difference in the median age and the choice of PCF was analysed using the Mann–Whitney U Test. The significance of the duration of an illness and age category was analysed using the Kruskal–Wallis test.

'Multimorbidity' was defined as “the co-occurrence of multiple chronic diseases and medical conditions within one person without any reference to an index condition” [31].

Main results

Socio-demographic profile

A total of 1630 RFEs were recorded from patients who presented to the eleven selected PCFs in the Anuradhapura district. Out of the total PCEs, 688 (42%) were from the private sector, 258 (16%) were from the DHs and PMCU, 464 (29%) were from BHs, and 220 (14%) were from TH. The median age of the patients was 39 years (interquatile range: 21–55), ranging from day one from birth to 94 years. The median age of patients who presented to a state PCF was significantly higher than that of a private PCF as determined by Mann-Whitney U Test (, p < 0.001).

The majority of children (1–12 years) and adolescents (13–17 years) presented to private sector facilities Table 1). Most older adults (65 years and older) presented to state sector PCFs. Both male and females mainly utilised state PCFs. There was a significant association between the choice of the PCF with patients' category of age, p < 0.001 and sex, p = 0.043.

Table 1.

The socio-demographic composition of the 1630 morbidity profiles of epidemiologically excluded COVID-19 primary care attendees in Anuradhapura District during the third wave of the COVID-19 pandemic

Socio-demographic characteristic	Variable category	Encounters at state sector primary care facilities n(%)	Encounters at private sector primary care facilities n(%)	Total encounters n(%)	Chi-square significance
Age	Newborns and Infants (below 1 year)	7(50)	7(50)	14(< 1)	p < 0.001
	Children (1–12 years)	88(35)	161(65)	249(15)
	Adolescents (13 years through 17 years)	24(34)	46(66)	70(4)
	Adults (18 years or older)	687(63)	411(37)	1098(67)
	Older adults (65 years and older)	136(68)	63(32)	199(12)
Sex	Male	440(60)	291(40)	731(45)	p = 0.043
	Female	502(56)	397(44)	899(55)

Table 1 includes age and sex-related variables, with subcategories for each variable. Statistical significance is indicated by the chi-square test results, with p-values provided for age and sex variables.

Acute RFEs in primary care

The PMPCs revealed 187 different RFEs among the patients (Additional file 2). RFEs were classified based on the nature of the encounters, namely “diagnostic” (n = 1448, 89%), “routine check-ups” (n = 135, 8%), “screening for diseases” (n = 34, 2%) and "immunisation" (n = 13, 1%). The state sector was mainly utilised for all these encounters significantly over the private sector (p < 0.001). The median duration of symptoms of an acute illness before the first PCE was three days (interquartile range:1–6.5), which differed between age categories (p < 0.001- Kruskal–Wallis test).

The top four most reported RFEs were upper respiratory tract infections (URTI, n = 154, 9%), dysuria (n = 73, 4.5%), heartburn (n = 72, 4,4%), and musculoskeletal symptoms/other (n = 63, 3.9%) (Additional file 3).

The median age of a patient with URTI was 23 years (interquartile range: 10–44). A higher incidence was seen in pediatric and young adult age groups. and in female patients (n = 85, 55%). Three patients presented with URTI but highly suspicious of COVID-19 were referred.

Most RFEs were reported in the dermatological (n = 294, 18%), respiratory (n = 274, 16.8%), and orthopaedic (n = 239, 14%) categories, while the fewest occurred in the urogenital category (n = 1, < 0.1%) (Fig. 1).

Fig. 1.

Systemic classification of reason for encounters and the choice of primary care facility

Figure 1 illustrates the systemic classification of patient encounters categorized by various symptoms. The data compares the number of patients treated for each symptom in the private sector versus the state sector. The symptoms are grouped into several categories, including dermatological, orthopedic, abdominal, renal, general processes (e.g.dressing/ pressure/ compression/ tamponade), respiratory, cardiological, neurological, general symptoms (e.g. fever), eye, endocrine, ear, gynecological, psychiatric, hematological, obstetric, and urogenital. The bar graph displays the number of patients on the x-axis, with the y-axis representing different symptom categories.

A diverse array of dermatological conditions was observed, ranging from abundant animal or human bites (n = 48, 2.9%), lacerations/cuts (n = 36, 2.2%), skin infections/post-traumatic (n = 34, 2.1%) to the least observed skin candidiasis and bruises/ contusions (n = 1, < 1%). Localised (n = 20) and generalised rashes (n = 17) and dermatitis (n = 19) were also abaundant in this region. Most dermatological conditions were either referred to a clinic or ward (n = 176, 59%), or undergone procedures (n = 78, 27%).

The most prevalent orthopaedic conditions were musculoskeletal symptoms/other (n = 63, 3.9%), musculoskeletal pains (n = 27, 1.7%) and, osteoarthrosis of the knee (n = 55, 3.4%). Bone fractures were present in six patients (0.4%). Five were referred to surgical and orthopaedic units in relevant hospitals for further investigation.

The obstetric (n = 5), haematological (n = 8), and urogenital (n = 1) categories reported the lowest number of RFEs. Only 0.6% (n = 10) of the patients reported psychological symptoms, which included depressive feelings (n = 5) and anxiety (n = 2).

Most respiratory (n = 222) and general symptoms (n = 66), were presented to the private sector (Additional file 4). Urban and suburban PCFs reported the most RFEs by URTI (n = 122) and dysuria (n = 34). Rural PCFs significantly encountered patients with knee osteoarthritis (n = 33).

NCDs and multimorbidity

More than one third of adults (n = 487, 37.5%) suffered from a self-reported NCD. The median age of presentation an NCD was 57 years (interquartile range: 66–45). A total of 297 (61%) NCDs were from female patients. Most patients with an NCD attended the state sector (n = 352, 72%) and suburban PCFs (n = 190, 39%). Hypertension (n = 235, 48%), diabetes mellitus (n = 184, 38%), dyslipidemia (n = 134, 28%), bronchial asthma (n = 68, 14%), chronic kidney disease (n = 41, 8%) and ischemic heart disease (n = 40, 8%) were the most observed self-reported NCDs in the study. Multimorbidity was reported in 40% (n = 195) of adult patients with an NCD and occurred more frequently in the middle-age categories (Fig. 2).

Fig. 2.

The burden of comorbidities among adult primary care attendees in the Anuradhapura district of Sri Lanka during the third wave of the COVID-19 pandemic

Figure 2 illustrates the number of comorbidities among patients across various age groups. The age groups are categorized as follows: 18–29, 30–40, 41–49, and 50–60. The y-axis represents the number of comorbidities, ranging from 0 to 5, while the x-axis shows the number of patients. The bars indicate the distribution of comorbidities within each age group, providing insights into the prevalence of comorbidities as patients age.

Hypertension-diabetes mellitus and hypertension-dyslipidemia were the most reported NCD combinations. One patient had presented with a multimorbidity of five NCDS, including diabetes, hypertension, heart disease, dyslipidemia and chronic kidney disease. Sixteen patients were suffered from four of the NCDs concurrently. Most multimorbid patients utilised the state sector PCFs over the private sector (p < 0.001).

Blood pressure screening was performed in 310 (23%) adult patients, and the median systolic and diastolic blood pressures were 130 mmHg (interquartile range: 119–150) and 80 mmHg (interquartile range 67–90), respectively. Body Mass Index (BMI) was assessed in 82 adults, and the median BMI was 23 kg/m² (interquartile range: 21–26).

Discussion

The present study investigated the common morbidity patterns in the primary care setting of the Anuradhapura district of Sri Lanka during the third wave of the COVID-19 pandemic. This study is among the few studies assessing primary care morbidity profiles of non-COVID-19 patients during the pandemic and contributes to evidence-based morbidity assessments in Sri Lanka [23, 32, 33].

The socio-demographic profiles of the primary care attendees during the pandemic reported a dynamic pattern in healthcare utilisation. The first COVID-19 case in Sri Lanka was reported on March 11 2020, and the first lockdown-type curfew was imposed on March 20 2020 [34]. The effect of COVID-19 changed over the next three years, resulting in three waves with fluctuating daily cases and mortality [35, 36]. However, the pandemic exerted increased strain on the local health system, overwhelming its capacity by the middle of the second wave [37]. Hospitals have considerably changed routine operational procedures, such as cancelling routine clinics, OPD, and surgeries [38]. The state OPDs had dedicated areas for detecting COVID-19 cases. In addition, telemedicine involved in triage -related to COVID-19 diagnosis and provision of e-prescriptions from the GPs [39]. The health advise for strict safe distancing in public places, anxiety and fear of COVID-19 contamination from crowds diverted vulnerable populations such as children and older adults to less restrictive private sector PCFs.

This study reported that among the epidemiologically excluded COVID-19 PCEs, female patients (55%) composed the majority, which is predictable considering the demographics in the pre-COVID period in Sri Lanka [33, 40]. Children and male patients mainly utilised the private sector for acute healthcare needs. Gender disparities in healthcare visits were observed in numerous pandemic-related studies [41, 42]. The healthcare authorities should improve more gender-oriented and age-adjusted primary care services during the pandemic to minimize healthcare disparities.

The findings of this study are consistent with the previous local studies reporting URTI as one of the most common RFEs [23, 32, 33]. Viral URTI usually exhibits a stable seasonal pattern in both the dry and wet zones of Sri Lanka [43]. Due to the emergence of co-viral infections, the epidemiology of viral URTI deteriorated during the pandemic [44]. Although respiratory conditions were reduced with the lockdowns and followed respiratory hygienic protocols, a 30% increase in pandemic-related respiratory conditions was observed [45]. The COVID-19 pandemic reinforced strategies for the early detection of infectious outbreaks through the present surveillance system [46]. More sentinel surveillance centres need to be established in local primary care settings to identify respiratory morbidity patterns and introduce early regulatory interventions for viral outbreaks.

A higher prevalence of dermatological conditions was observed in this district, similar to country-level data from the pre-COVID-19 period [23, 32, 33]. The tropical climate and higher humidity from October to January resulted in increased incidence rates of fungal skin infections [47] and animal bites in the region [48]. The morbidity profile of outpatient dermatological conditions changed during the COVID-19 pandemic based on country-specific disease control measures, the effect of COVID-19 on healthcare staff [49] and tele-dermatological consultations [50]. About three per cent of dermatological conditions were caused by injuries and trauma. Annual health statistics report a 14% drop in traumatic injuries presented to OPDs during the pandemic but a marked increase in home-related injuries caused by lockdowns and travel restrictions [51]. The availability of vaccination and surgical facilities free of charge and the state doctors’ technical compliance have encouraged patients to utilise state OPDs more for animal bites. Primary care doctors need comprehensive training to manage common dermatological conditions effectively, enabling them to address a significant amount of dermatology-related encounters.

This study revealed a marked reduction in psychiatric conditions among primary care attendees, an unexpected observation compared to the pandemic period. With the COVID-19 pandemic, a significant burden of psychological issues was observed among frontline workers (depressive symptoms in 39%, anxiety in 55% ) and patients (depression in 18.6% and anxiety in 11.2%) [52, 53]. Although specialised psychiatric outpatient routine care is provided at secondary and tertiary state hospitals in Sri Lanka, a 16% decline in psychiatric clinic attendance was reported in 2021 compared to 2020 [51]. The identification of mental health conditions in primary care significantly reduced during the COVID-19 pandemic [54]. A tertiary care-based study in South Korea found an association between the reduction in daily psychiatric conditions and the increasing daily number of newly confirmed COVID-19 cases [55]. Lockdowns, social distancing, fear of contamination, telepsychiatry and pandemic-enhanced fear of judgment and stigmatization would have led to reduced use of outpatient and clinic care for mental health-related issues. Comprehensive and accessible services for mental health disorders should be available within PCFs to help patients overcome barriers and increase healthcare seeking during the pandemic.

This study highlights the importance of delivering comprehensive care for patients with NCDs and multimorbidity in state and private sector PCFs during a pandemic. During the COVID-19 outbreak, routine clinics were drastically disrupted, and routine medications were dispatched to the homes as a strategy to continue to care for patients with NCDs [56]. This study's findings indicate that NCDs are a significant health concern, affecting one-third of adult patients. Cardiovascular diseases, one of the highly prevalent NCDs in the country contributed to 83% of the local deaths in 2018 [57]. The current primary care setting reported a similar morbidity burden of hypertension and diabetes compared to the pre-pandemic period [40]. Multimorbidity reported from the current rural primary care setting is lower compared to prepandemic urban studies [40]. A similar pattern of increased morbidity due to NCDs was observed in other South Asian studies [58]. Managing comorbidities was a key strategy postively linked to improved mortality from COVID-19 [59]. However, the interrupted healthcare services raised the unmet need for routine care, leading to an increase in non-COVID-19-related morbidity and mortality during and after the pandemic [60]. During pandemics, routine outpatient care must continue by ensuring adequate human and physical resources while implementing prevention strategies aimed at high-risk and high-demand populations.

Limitations

Selection bias might affect the PMPCs collected from the private sector, since the convenience sampling method was used due to lack of a GP register in the district. Excluding suspected COVID-19 patients leads to an underestimation of respiratory morbidity. WHO recommended triage and case definitions were adapted for epidemiologically exclusion of COVID-19 at PCFs. However there remainsa possibility of unconfirmed COVID-19 cases presenting with respiratory and non-respiratory symptoms.

Conclusion

Dermatological, respiratory, and orthopedic conditions represented a significant proportion of primary care morbidity among the epidemiologically excluded COVID-19 patients during the pandemic. The pandemic has led to a reduction in PCEs due to genito-urinary, haematological, obstetric, and psychiatric conditions, which need further evaluation. A disparity in the utilisation of state and private sector PCFs was observed based on the nature and severity of the RFEs and the age and sex of the patient. It is recommended that primary care settings be modified to provide more accessible and comprehensive services that are tailored to different genders and age groups, especially during pandemics. There is an increasing trend in multimorbidity within primary care, which requires primary care-level opportunistic interventions for disease prevention, while tailoring for the increasing demand during pandemics.

Abbreviations

BH

Base hospital

COVID-19

Coronavirus disease

DH

Divisional hospital

GP

General practices

ICPC

International Classification of Primary Care

MoH

Ministry of Health

NCD

Non-communicable diseases

OPD

Outpatient department

PCE

Primary care encounters

PCF

Primary care facilities

PHEIC

Public Health Emergency of International Concern

PMCU

Primary medical care unit

PMPC

Patient Morbidity Profile Collector

RFE

Reason for the encounter

TH

Teaching hospital

URTI

Upper respiratory tract infection

Authors’ contributions

All authors contributed to the conceptualising, methodology, investigation, data analysis, visualisation, and validation of the study findings. P.A. contributed to funding acquisition and writing the original draft. S.B.A., S.S., and P.H.G.J.P. contributed to supervising, reviewing, and editing the manuscript.

Funding

The Rajarata University Research Grant supported this work under grant number RJT/R&PC/2021/R/FMAS/01. P.A. acquired the funds. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data availability

TThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the Faculty of Medicine and Allied Sciences Ethics Review Committee, the Rajarata University of Sri Lanka, under ERC/2020/66. Informed consent was obtained from all adult participants, and assent was obtained from the guardians of children younger than 18. An information sheet in Sinhala, Tamil and English was shared among the participants to explain the aim of the study. No incentives were given to the study participants during the data collection. Before data collection, approval from healthcare institutions was obtained from the directors and medical officers of state hospitals and private sector primary care facilities.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.