Susceptibility to Malaria During the Prevention of Re-introduction/Re-establishment Phase in Sri Lanka

doi:10.21203/rs.3.rs-53968/v1

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Susceptibility to Malaria During the Prevention of Re-introduction/Re-establishment Phase in Sri Lanka

https://doi.org/10.21203/rs.3.rs-53968/v1

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published 27 Mar, 2022

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Background

Susceptibility to malaria is the predisposition of populations to be infected by malaria parasites. It is influenced by the age-sex composition, parasitaemia, immunity, pregnancy status, type of residency, socio economic status, history migration of the population; broadly biological and generic factors. As imported cases are still being reported and one introduced malaria case was reported in 2018, prevention of re-introduction/re-establishment of malaria is a challenge for Sri Lanka. Addressing susceptibility to malaria is crucial for planning and implementing appropriate interventions to sustain malaria-free status of the country. The aim of this study was to assess susceptibility to malaria during the prevention of re-introduction/re-establishment phase in Sri Lanka.

Methods

A national survey was conducted among 3,454 households. A multistage cluster sampling technique was used to select the households. Susceptibility was assessed based on a developed conceptual framework adapted from Kienberger and Hagenlocher (2014), and an interviewer-administered questionnaire. Data analysis was done using SPSS version 20 package.

Results

There was zero prevalence of malaria parasites both in the general population and among fever patients. The proportion of population who had been overseas within the last 3 years in the urban sector (4.5%, n=99) was significantly higher than that of rural (2.8%, n=288) and estate sectors (0.2%, n=2) (χ₄²=66.103; p<0.001) and it significantly declined with the wealth index up to the 4^thquintile with a slight rise in the 5^th quintile (χ₈²=60.985; P<0.001).

Conclusions

Zero prevalence of malaria parasites supports the malaria-free status of the country and pooled blood samples may be tested during reactive case surveillance as a cost-effective approach. Urban and upper socioeconomic class population should be targeted for screening and awareness programmes on prevention of re-establishment of malaria.

As susceptibility is a dynamic phenomenon, its assessment should be done periodically. Combining it with the resilience and receptivity, social vulnerability and risk of re-introduction/re-establishment of malaria could be assessed.

Infectious Diseases

Malaria Parasite

Susceptibility

Prevention of Re-introduction/Re-establishment of malaria

Sri Lanka

The burden of malaria has decreased in many countries over the last decade due to accelerated prevention and control activities. According to WHO estimates (2019), there were about 228 million cases of malaria and an estimated 405,000deathsdue to malaria in 2018. Over the last decade, 10 countries have been certified by the WHO as malaria-free: Morocco (2010), Turkmenistan (2010), Armenia (2011), Maldives (2015), Sri Lanka (2016), Kyrgyzstan (2016), Paraguay (2018), Uzbekistan (2018), Argentina (2018) and Algeria (2019) (1).However, there has been no significant progress in reducing the global malaria burden during 2015-2017(2).The achievement of malaria elimination carries with it the risk of malaria re-establishment. The risk of re-establishment of malaria is a challenge for countries that have eliminated malaria until global eradication of malaria.

Sri Lanka was certified as malaria-free in September 2016 with no indigenous cases of malaria being reported since November 2012. There was one introduced malaria case in December 2018. Less than 100 imported malaria cases have been reported from 2013 onwards, among both Sri Lankans traveling overseas and foreign nationals coming into the country, maintaining the susceptibility of the country to malaria in a vulnerable environment (3, 4, and 5).Thus, there is a potential threat for the re-establishment of malaria in Sri Lanka. Hence, assessing the susceptibility of the population to malaria is crucial for planning and implementing prevention of re-establishment of malaria programmes.

Susceptibility to malaria during the prevention of re-establishment phase in Sri Lanka

Population risk and vulnerability to malaria is determined by the degree of susceptibility of the population. Susceptibility is the predisposition of being negatively affected by an outbreak in terms of biologic and generic factors. It is determined by biological susceptibility factors such as age and sex and generic susceptibility factors such as migration and socio economic status of the population (6).

Migration of people between Sri Lanka and malaria endemic countries is the most important susceptibility factor in the prevention of re-establishment of malaria phase. It is the only way in which malaria parasites can be re-introduced into the country other than infected mosquitoes entering into the country. The risk groups in whom imported malaria was reported include; asylum-seekers, local fishermen who returned from Sierra Leone, army personnel returning after serving in UN peace-keeping missions in Haiti and South Sudan, irregular migrants from Myanmar, foreign skilled and unskilled labor working in several parts of the country, Sri Lankan nationals engaged in business in African countries and in India, tourists and foreign labour (7).

The importance of social factors that make some groups or individuals more susceptible to infection and more limited in their ability to respond to illness than others was highlighted in a study of social vulnerability assessment of vector-borne diseases (8).Malaria is no more a ‘disease of the poor’ in countries like Sri Lanka because of the influence of social class on migration. Children and pregnant women are more susceptible to malaria. Immuno-compromised patients such as HIV/AIDS and tuberculosis patients are the more susceptible groups, in general (9).

Wessen (1972) states that "only in retrospect has it become fully clear that the failure of malaria eradication was in large part a failure at the social and organization levels”(10).Several studies have shown that socio-cultural factors should have been taken into account at the initial stage of the programme and for the recommendation of the programme design(11).

There are no published studies on susceptibility to malaria during the prevention of re-introduction/re-establishment phase in Sri Lanka. There are some studies which assessed the influence of certain social aspects on malaria control during the control phase. This study assessed susceptibility to malaria in the prevention of re-introduction/re-establishment phase in Sri Lanka.

This cross sectional study covered the whole country.

Study setting

Sri Lanka is an island in the Indian Ocean, southeast of India, with a total land area of 65,610 km². The population of Sri Lanka is approximately 21 million. The district population structure shows that approximately 18% of the population is resident in urban areas (12). Administratively, Sri Lanka is divided into 9 provinces, and the 9 provinces are further divided into 25 districts. The Medical Officer of Health (MOH) is responsible for preventive health services in a defined area. The MOH area is further divided into Public Health Inspector (PHI) and Public Health Midwife (PHM) areas (13).

The study population consisted of members of all households in all the districts of Sri Lanka. A household was defined as ‘one or more persons living together and who have a common arrangement for provision of food living in a housing unit’ (14).Public places like homes for elders, orphanages, and religious homes were excluded.

Conceptual setting: Susceptibility as a domain of the social vulnerability framework

Social vulnerability is a recently developed concept on communication of natural hazards and disasters and is partially the result of social inequalities. Social factors influence susceptibility of various groups and their ability to respond to harm (15). Social vulnerability is not only due to exposure to hazards alone, but also depends on the sensitivity and resilience of the system to prepare, cope and recover from such hazards (16). Piers et al. (2005) define social vulnerability as “the characteristics of a person or group in terms of their capacity to anticipate, cope with, resist and recover from the impact of a natural hazard. Methods for the Improvement of Vulnerability Assessment in Europe (MOVE) project have elaborated a framework in the context of natural hazards and climate change (17).

Susceptibility domains were adapted from the conceptual framework for social vulnerability to vector borne diseases by Kienberger & Hagenlocher (2014) (Figure 1). Kienberger and Hagenlocher (2014) define social vulnerability to malaria as the predisposition of the population to acquire malaria (7, 18, and 19). It is characterized by different interrelated domains, generic and biological susceptibility, and lack of resilience. The generic and biological susceptibility factors predispose a community to the malaria burden. The resilience factors determine the community’s ability to anticipate, respond to, cope with, or recover from the malaria burden.

Adapted framework for susceptibility to malaria during the prevention of re-introduction/re-establishment phase

Based on the review of literature and extensive consultations with malaria experts, social vulnerability indicators of malaria during the prevention of re-introduction/re-establishment phase in Sri Lanka were identified under two main domains; susceptibility and resilience. The susceptibility domain, assessed in this study, is shown in Figure 2.

The susceptibility domain includes biological and generic factors. Age, gender, pregnancy, prevalence of parasitaemia, and immunity status/past history of malaria during the past 3 years were considered as biological susceptibility indicators. Migration, residency of the population (urban/rural/estate) and socio-economic status were considered as generic susceptibility indicators. Table 1 depicts the comparison of susceptibility indicators in both frameworks and the justification for the selection of the indicators.

Table 1

Comparison of susceptibility indicators between framework of Kienberger and Hegenlocher (2014) and the framework used in this study with justification
Susceptibility Subdomains	Kienberger and Hagenlocher, during control phase, 2014 (East Africa)	Adapted for PoR* phase 2016 (Sri Lanka)	Justification for the selection of indicators
Biological Susceptibility	· Number of women of childbearing age · Number of children under 5 · Prevalence of stunting in children under 5 · HIV prevalence among 15-49 year olds · Altitude (proxy for immunity)	· Age · Gender · Pregnancy · Immunity (past history of malaria within last 3 years) · Parasitaemia	Age <5 &>65 and females are susceptible groups for malaria. Pregnant mothers and non-immune persons are more prone to get malaria. Parasitaemia among the population leads to local transmission especially when asymptomatic.
Generic Susceptibility	· Number of women · Population change · Travel time to closest urban center · Number of people living on less than USD 2 per day(Poverty)	· Place of residency(urban, rural and estate sectors) · Migration · Socio-economic status (wealth index)	Malaria is a disease of rural populations in developing countries History of travel to malaria endemic countries is an important factor to introduce malaria There are differences in access to health care, immunity and migration patterns in different socio-economic groups
*PoR refers to prevention of re-introduction/re-establishment

Development of the data collection tool

Based on the conceptual framework, a household survey questionnaire was developed to assess susceptibility to malaria during the prevention of re-introduction phase. The following details were collected: identification of households including socio demographic characteristics of household members, and household characteristics to assess socio-economic status (SES) of the household. The assessment of socio economic status (SES) was based on the Demographic Health Survey (DHS) format used worldwide (20). Wealth index was used to classify the socio-economic status of participants. The source of water, toilet facilities, type of fuel used, material used for the floor of the house, mode of transport, access to mass media, electricity in the household, possession of some household items, and ownership of agricultural land and farm animals were included in the questionnaire. The migration history of household members within the last 3 years was included.

Validation of tool

Questionnaires along with the adapted conceptual framework were distributed among experts to assess judgmental validity (face and content) of the questionnaire. Face and content validities were assessed by malaria experts. The questionnaires were translated using standard methods and pre-tested in the field; necessary changes were made accordingly.

Sample size and sampling

For sample size calculation to assess susceptibility to malaria, there are no guidelines or literature that could be used in the prevention of re-introduction phase. Hence, we considered the number of Sri Lankans traveling overseas as the most important variable to consider in sample size calculation. In order to estimate the proportion of Sri Lankans traveling overseas in a year as 6% with a margin of error ranging from 4.5%-7.5% for a 95% confidence interval, assuming a design effect of 3.2 for using cluster sampling (cluster size of 12), and a non-response rate of 10%, a sample of 3424 households had to be surveyed(21). Households were selected equally from among the 25 districts proportionate to urban, rural and estate sectors. Therefore, from each district 137 households had to be surveyed from 11.4 clusters per district. Hence, twelve clusters of 12 households from each cluster were randomly selected from each district (25districts × 12 PHM clusters ×12 households) to give a total sample size of 3600 households.

A stratified multistage cluster sampling method was used with the primary sampling unit being MOH areas. Sampling units were selected from all 25 districts proportionate to size of the urban, rural and estate sectors of the relevant districts. There were 6-8 MOH areas randomly selected from each district. 12 Public Health Midwife (PHM) area clusters were randomly selected from the selected MOH areas (on average 2 PHM clusters per MOH area). From each PHM area cluster, the starting point of the household survey was randomly selected by dropping a headed pin on the PHM area map and the house closest to the pointed edge was selected. After the first house was identified, every tenth house to the left of the selected house was chosen until 12 households for that PHM area were surveyed.

Data collection

The validated tool was used in the household survey in all districts of the country. The data collectors were trained on sampling technique and the importance of sampling during the training sessions; in the field they were supervised by malaria officers or the principal investigator. The manual for interviewers and Global Positioning System (GPS) data collection were prepared based on the guidelines for conducting Malaria Indicator Surveys as given by the Roll Back Malaria Initiative and given to all data collectors for easy reference. Fieldwork was carried out from July 2016 to March 2017. The head of the household was interviewed. If the head of the household could not be interviewed (e.g.: household closed), the next closest household was selected. If the head of the household had responded and was not available at the time of survey, the contact number was obtained; the household head was approached a second time, after confirmation of his/her presence, mainly during weekends. If the second attempt failed, the house was visited for a third time; if the head of the household could not be contacted a third time, it was considered a non-responder.

Screening for malaria was done according to the standard operating procedure for parasitological screening for malaria of the Anti Malaria Campaign (AMC), Sri Lanka. Microscopy was done by a trained Public Health Laboratory Technologist (PHLT) from each region. Data collection was supervised by regional malaria officers, medical officers attached to Anti Malaria Campaign/Headquarters and the principal investigator.

Data Analysis

Data analysis was done using IBM SPSS statistics version 20 software package. Descriptive analyses were used to describe socio-demographic characteristics. Socio-economic status of the population was assessed by the wealth index using exploratory factor analysis. Principal components analysis (PCA) was used to generate a weighted score based on household assets. The wealth index for a household is the linear combination defined as the principal component variable across households or individuals with a mean of zero and a variance of one, corresponding to the ‘Eigenvalue’ of the correlation matrix. The estimated wealth index was based on a population of 13,365 resident in 3,454 households located in all districts in Sri Lanka.

The variables selected for derivation of the wealth index were based on the methodology used by the Department of Census and Statistics in their routine surveys (DHS) (14) and in the guidelines to conduct a Malaria Indicator Survey (22).

Descriptive analysis was used to describe variables included in ascertaining biological susceptibility. The association between wealth index, sector of residency and history of migration was analyzed using the chi-square test.

3454 out of the selected 3600 households (response rate of 95.9%) responded within two consecutive visits. Figure 1 shows the geographic distribution of the surveyed households.

Characteristics Of The Population Of The Household Survey

The mean age of the head of the household was 51 years (SD = 13.9), the age ranging from 19 to 92 years. The majority of the heads of households were males (n = 2758; 79.8%); most heads of households were 41–60 year of age (n = 1317, 38.1%) (Table 2).

Table 2

Distribution of heads of households and household members by age and sex
Age Group	Male No. (%)	Female No. (%)	Total No. (%)
Heads of the Households
< 20	6 (0.2)	0(0.0)	6(0.2)
21–40	759(22.0)	135(3.9)	894(25.9)
41–60	1317(38.1)	317(9.2)	1634(47.3)
> 60	676 (19.6)	244(7.1)	920(26.6)
Total	2758(79.8)	696(20.2)	3454(100)
Household Members
< 5	627 (9.5)	536(8.0)	1163(8.7)
6–20	1556(23.5)	1455(21.6)	3011(22.5)
21–35	1529 (23.0)	1684(25.0)	3213(24.0)
36–50	1311 (19.7)	1389(20.7)	2700(20.2)
51–65	1098 (16.5)	1157(17.2)	2255(16.9)
> 65	519(7.8)	504(7.5)	1023(7.7)
Total	6,640(100)	6,725(100)	13,365(100)

The socio-demographic characteristics of heads of households are shown in Table 3. The majority of heads of households was Sinhalese (n = 2,240, 64.9%), married (n = 3032, 87.8%) and employed (n = 2,995, 86.8%). Seventy eight percent (n = 2693) of heads of households had more than primary education while 3.9% (n = 133) had no schooling. The majority of families (n = 2,223, 64.4%) had a monthly income between Sri Lanka Rupees (SLR) 10,000–50,000.

Table 3

Socio-demographic characteristics of Heads of households
Characteristic		Number (%) (n = 3454)
Ethnicity	Sinhalese	2240 (64.9)
	Tamil	1001 (28.9)
	Muslim	210 ( 6.1)
	Other	3 ( 0.1)
Marital status	Married	3032 (87.8)
	Unmarried	62 ( 1.8)
	Divorced/Separated	57 ( 1.7)
	Widowed	303 ( 8.8)
Educational level	No schooling	133 ( 3.9)
	Primary (Grade1-5)	628 (18.2)
	Secondary(Grade 6–10)	1019 (29.5)
	Completed GCE¹O/L²or equivalent	1060 (30.7)
	Completed GCE A/L³or equivalent	532 (15.4)
	Degree and above	82 ( 2.4)
Occupation	Employed	2995(86.8)
	Retired	187 ( 5.4)
	Unemployed	272 ( 7.8)
Monthly Family Income (SLR)⁴	<Rs 10,000/-	945(27.4)
	Rs 10,001/- - Rs 30,000/-	1505(43.6)
	Rs 30,001/- - Rs 50,000/-	718(20.8)
	Rs 50,001/- - Rs 70,000/-	212( 6.1)
	Rs 70,001/- - Rs 100,000/-	64 ( 1.9)
	>Rs 100,000/-	10 ( 0.3)
¹General Certificate of Education, ²Ordinary Level,³Advanced level ⁴ SLR refers to Sri Lankan rupees (1 USD ≈ SLR 150 at time of study)

The majority of households (n = 2,685, 77.7%) and the household members (n = 10,231, 76.5%), were from the rural sector (Table 4).

Table 4

Distribution of households by urban, rural and estate sectors
Sector	Number of households (%)	Number of household members (%)
Urban	573 (16.6)	2201 (16.5)
Rural	2685 (77.7)	10231 (76.5)
Estate	196 (5.7)	933 (7.0)
Total	3454(100.0)	13365 (100.0)

The age-sex distribution of household members is shown in table 5. The sex distribution within each age category was similar.

Susceptibility indices

Biological susceptibility indices: Table 6 gives the biological susceptibility indices. There were no malaria cases detected among the population or among fever patients, and no one gave a past history of malaria within the last 3 years of the survey.

Table 6

Biological susceptibility indices
Susceptibility indicators
Susceptibility indicators	No. (%)	95% CI
Age under 5 and over 65	2,186 (16.4)	15.7–16.9
Male gender	6,640 (49.7)	48.8–50.5
Pregnant mothers	126 (35.6 *)	33.6–47.7
Parasitaemia status Slide positive cases among population	0 (0.0)	-
RDT** negative	6,069(100.0)	-
RDT** not done	7,296 (54.6)	53.7–55.4
Microscopy negative	12,420 (100.0)	-
Microscopy not done	945 (7.1)	6.4–7.5
Slide positive cases among fever patients	0 (0.0)	-
Past history of malaria within 3 years	0 (0.0)	-
Pregnancy per 1000 was calculated for women of child bearing age (15–49 years) *Rapid Diagnostic Test

Generic susceptibility indices

Generic susceptibility indices considered were sector of residency, been abroad within last 3 years and wealth quintiles. The proportion of population who had been abroad within the last 3 years in urban sector (4.5%, n = 99) was significantly higher than that of rural (2.8%, n = 288) and estate sectors (0.2%, n = 2) (χ₄² = 66.103; p < 0.001). (Table7).

Table 7

Distribution of population who had been abroad by sector of residence
Sector of residence	Been abroad within last 3 years						Total No. (%)
Sector of residence	Yes No. (%)	No No. (%)			Don’t know No. (%)		Total No. (%)
Urban	99 (4.5)		2082(94.6)	20 (0.9)		2201(100.0)
Rural	288 (2.8)		9773 (95.5)	170 (1.7)		10231(100.0)
Estate	2 (0.2)		931(99.8)	0(0.0)		933 (100.0)
Total	389 (2.9)		12786 (95.7)	190 (1.4)		13365 (100.0)
χ₄² = 66.103; p < 0.001

The proportion of the population who had been abroad within the last 3 years declined up to the 4th quintile; there was a slight rise in the 5th quintile (χ82 = 60.985, P < 0.001) (Table 8).

Table 8

Association between having travelled abroad and wealth index
Wealth Quintiles	Been abroad within last 3 years			Total
Wealth Quintiles	Yes No. (%)	No No. (%)	Don’t know No. (%)	Total
1st Quintile	109 (4.1)	2530 (94.5)	38 (1.4)	2677 (100.0)
2nd Quintile	89 (3.3)	2583 (96.1)	15 (0.6)	2687 (100.0)
3rd Quintile	63 (2.4)	2559 (96.3)	35 (1.3)	2657 (100.0)
4th Quintile	61 (2.3)	2578 (96.4)	34 (1.3)	2673 (100.0)
5th Quintile	67 (2.5)	2536 (94.9)	68 (2.5)	2671 (100.0)
Total	389 (2.9)	12,786 (95.7)	190 (1.4)	13,365 (100.0)
χ₈² = 60.985, P < 0.001

The important finding of zero prevalence of parasitaemia and there being no malaria infections during the last 3 years among the surveyed population provide unequivocal evidence that the country is malaria-free. As expected, persons’ resident in urban areas and in the first wealth quintile had traveled more than others. There was a decreasing trend in the proportion of persons traveling with increasing wealth quintile except in the lowest quintile which may be due to migration for labour.

The vulnerability of the population to malaria is high when people have little immunity. A study done during the pre-elimination phase in two formerly high endemic districts of Kurunegala and Anuradhapura showed that around 1.4% had a past history of malaria in the preceding 5 years of the survey. In the same study, there were no asymptomatic cases detected using a simple, highly sensitive nested PCR test (23).

A number of epidemiologic studies support the link between malaria and migration. It has been shown that the prevalence of malaria is higher in communities with higher levels of immigration in the Amazon (24). The migration of non-immune populations from Sri Lanka to malaria endemic countries influences the importation of malaria parasites into the country. The impact could be significant as a single infection can lead to introduced cases and secondary infections among non-migrants of their communities, thus leading to the re-introduction and possibly re-establishment of malaria in to the country. Thus, it is important to consider the migrant population in planning prevention of re-introduction/re-establishment programmes.

Kienberger S and Hagenlocher M (2014) who originally developed the conceptual framework for social vulnerability to vector borne diseases did spatial-explicit modeling of social vulnerability to malaria in East Africa (18). They found that biological susceptibility indicators contribute more to malaria vulnerability. High levels of malaria vulnerability were found in the highlands where the immunity of the population was very low and regions with lack of access to education and health services. Lower values were found in regions with relatively low poverty, low population pressure and low conflict-density.

Bizimana et al. (2015) during the control phase in Rwanda reported that some districts have high social vulnerability to malaria (25). They found that the most influential susceptibility indicators were; population change (r = 0.729), average number of persons per bedroom (r = 0.531), number of households affected by droughts and famines (r = 0.591), area used for irrigation (r = 0.611), bed net ownership (r = − 0.398) and poor housing wall materials (r = 0.378).They suggested that vulnerability assessments be done by combining environmental and social drivers to achieve an integrated and complete assessment and that spatial assessment be done without considering administrative boundaries.

Literature suggests that children under 5, persons more than 65 years and pregnant females are more susceptible to get malaria. The relationship between malaria transmission and age is well documented by Molineaux (1988); Snow et al. (1997); and Snow & Marsh (1998) (26). In high transmission settings, the incidence of malaria peaks in early childhood and then declines with increasing age with acquired immunity due to exposure to malaria infections. In moderate transmission settings, the age of peak transmission is a little later than childhood, while in low transmission settings, such as in Sri Lanka in the past, the risk of infection remains same across all ages (26). However, Muhlberger et al., (2003) report that the risks of death and development of severe complications due to falciparum malaria increase with age in European patients who are non-immune to malaria. Among the surveyed population in the present study, 16.4% (n = 2186) of the population was under 5 or over 65 years (27).

Pregnant females are more prone to get infections as their immunological responses are lower due to the pregnancy (28). In this study, the pregnant population comprised 35.6 per 1000 (n = 126) females of child bearing age (15–49 years).

Although we considered age, sex and pregnancy status as indicators for susceptibility to malaria as in a control phase, our results suggest that, in a prevention of re-introduction phase, these indicators may not be valid indicators of susceptibility to malaria. Based on our results, we surmise that the most important factor for assessing susceptibility to malaria in the prevention of re-introduction phase is migration; recent travel overseas, wealth quintile and sector of residence are good indicators to monitor susceptibility to malaria.

As Sri Lanka was one of the first tropical countries in the recent past to eliminate malaria, there were no comparable studies to adapt methodological reproducibility in similar settings. Concept development depended heavily on the views of malaria experts in the country, studies done by experts in malaria control settings and disaster vulnerability studies. Other than vector and parasite factors, human factors in the form of social vulnerability are important to prevent re-introduction/re-establishment of malaria as highlighted in the conceptual framework.

This national survey to assess susceptibility to malaria during the prevention of re-introduction/re-establishment phase is the first study attempted in a country certified as malaria-free. The study did not specifically include or give priority for selection of specific migrant groups such as refugees, migrant workers residing in construction sites and factories. Refugee returnees from India in the Northern Province were included in the household survey as they were already re-settled and included in the sampling frame. It would have been better to include data on aspects such as areas of returning refugees, immigrant labour and congregations of other high risk groups to assess generic susceptibility. Identifying these high risk populations is a challenge as there are illegal migrant workers in the country. Thus, there is an urgent need to identify migrant populations in the most vulnerable and receptive areas with potential for local transmission of malaria and to target interventions accordingly to prevent re-introduction and re-establishment of malaria in the country. One good example in the past was the asylum seekers residing in the Gampaha district, which was previously not endemic for malaria, where 17 imported malaria cases were reported in 2013; further local transmission was prevented through a high risk group screening programme (7).

The results from this study are important for public health policy and decision makers in Sri Lanka. It also highlights the need for widening the scope of traditional malaria control, elimination and prevention of re-introduction/re-establishment programmes to include new aspects such as monitoring migration patterns and special areas where high risk groups such as refugees and migrant foreign labour reside. This is a challenge and may require developing new procedures and task shifting of staff during the transition from a control to a prevention of re-introduction/re-establishment phase. This special assessment provided timely evidence to plan and implement the prevention of re-introduction/re-establishment of malaria programme in Sri Lanka; the findings will improve the efficiency, effectiveness and sustainability of PoR interventions through advocacy, social mobilization and inter-sectoral collaboration to optimize the allocation of limited resources and health infrastructure at national level.

Microscopy or RDT examination could not be done in some persons as they did not give consent for the procedure. Despite this limitation, it is unlikely that the result would have been different as a large number of persons were examined. Microscopy was conducted by PHLTs in the field. Although the possibility of making errors in diagnosis exists, it is unlikely as senior PHLTs who regularly undergo quality assurance and quality control assessment conducted by the central Anti Malaria Campaign examined all the blood smears.

Urban residents and upper socioeconomic class persons are more likely to bring the parasite into the country as a significant number of them travel overseas. In addition, migration patterns and areas where known high risk groups reside should be monitored. This will require new procedures to be developed and task shifting of existing staff as the programme transits from a control to a prevention of re-introduction/re-establishment phase.

Zero prevalence of malaria parasites in the general population and no indigenous cases of malaria being reported within the 3 years prior to the survey re-confirms the malaria-free status of the country.

As the susceptibility to malaria is a dynamic phenomenon, susceptibility to malaria should be assessed every 3–5 years. As baseline data is now available, the Regional Malaria Officers can update the required information. Susceptibility assessments should combine with resilience and receptivity to assess social vulnerability and risk of malaria during prevention of re-introduction/re-establishment of malaria.

A/L: Advanced level, AMC: Anti Malaria Campaign, DHS: Demographic Health Survey, GCE: General Certificate of Education, GPS: Global Positioning System, IOM: International Organization for Migration, MOH: Medical Officer of Health, MOVE: Methods for the Improvement of Vulnerability Assessment in Europe, O/L: Ordinary Level, PCA: Principal components analysis, PHI: Public Health Inspector, PCR: Polymerase Chain reaction, PHM: Public Health Midwife, PHLT: Public Health Laboratory Technologist, PoR: Prevention of Re-introduction /Re-establishment, RDT: Rapid Diagnostic test. SES: socio economic status, SPSS: Statistical Package for Social Sciences, UN: United Nations, WMR: World Malaria Report, WHO: world health Organization, SLR: Sri Lanka Rupees

Acknowledgements

The authors wish to acknowledge the Director and all regional and central staff of Anti Malaria Campaign, Sri Lanka for the support given during data the survey.

Authors’ contributions

JH was responsible for the concept, design, acquisition of data, analysis, interpretation of data and writing the manuscript under the supervision of RW.

RW contributed to intellectual input and critically revised the paper.

Funding

The funding for the study was provided by the Anti Malaria Campaign, Ministry of Health through a GFATM grant.

Availability of data and materials

All data generated or analyzed during this study are available on request

Ethical approval and consent to participation

Ethical clearance was obtained from the Faculty of Medicine, University of Colombo. During the survey, the objectives of the study were explained to heads of households and confidentiality assured. Participants were explained regarding blood screening for malaria and its importance, using a written participant information sheet. Written consent for voluntary participation was obtained before the data collection.

Permission to conduct the study was obtained from the Provincial Directors of Health Services and Regional Directors of Health Services, local government authorities and Medical Officers of Health.

Consent for publication

Not applicable

Competing interests

The authors have declared that they have no competing interests.

Author details

J Hamsananthy, Acting Consultant Community Physician, Deputy Director General /Laboratory Services Unit, Ministry of Health, Sri Lanka

A R Wickremasinghe, Professor of Public Health, Department of Public Health, Faculty of Medicine, University of Kelaniya

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Susceptibility to Malaria During the Prevention of Re-introduction/Re-establishment Phase in Sri Lanka

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Abstract

Figures

Background

Susceptibility to malaria during the prevention of re-establishment phase in Sri Lanka

Methods

Study setting

Conceptual setting: Susceptibility as a domain of the social vulnerability framework

Adapted framework for susceptibility to malaria during the prevention of re-introduction/re-establishment phase

Validation of tool

Sample size and sampling

Data collection

Data Analysis

Results

Characteristics Of The Population Of The Household Survey

Susceptibility indices

Generic susceptibility indices

Discussion

Limitations

Conclusions

Abbreviations

Declarations

Acknowledgements

Authors’ contributions

Funding

Availability of data and materials

Ethical approval and consent to participation

Consent for publication

Competing interests

Author details

References

Status:

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