An evaluation of the measles surveillance system, and descriptive epidemiology of measles in Kenya (2020 - 2021)

Francis Muoka Ndonye^1,&, Caren Ndeta¹, Rosemary Nzunza², David Kareko³, Hilary Limo³

 

¹Field Epidemiology and Laboratory Training Program, Nairobi, Kenya, ²Kenya Medical Research Institute, Centre for Virus Research, Nairobi, Kenya, ³Minstry of Health, Division of Disease Surveillance and Response, Nairobi, Kenya

 

 

^&Corresponding author
Francis Muoka Ndonye, Field Epidemiology and Laboratory Training Program, Nairobi, Kenya.

 

 

 

 

Measles is an infectious viral disease that mostly affects children, however, it can affect people of all ages [1]. The measles virus has a reproduction number (the number of people infected from a single infectious case) of 12-18, making it the most infectious virus in the world [2]. The World Health Organization (WHO) set a target of eliminating measles in five out of its six regions by the year 2020 [3]. Elimination is achieved following the confirmed absence of measles transmission in a given geographical area for at least a year when an effective surveillance system is in place [4]. The annual global incidence rate of measles increased from 18 per million population in 2016 to 120 per million population in 2019 [5]. The WHO-African Region (WHO-AFRO) has consistently recorded the highest incidence of measles [6]. Incidence of measles in Kenya ranged between two (2) to 65 per million population between 2003 and 2016 [7]. The global case fatality rate of measles is 3-5% but can be as high as 10% in epidemics [8].

 

Public health surveillance is the systematic ongoing collection, collation, analysis, interpretation and dissemination of health-related data to inform public health action [9]. Measles is one of the 46 priority diseases in Kenya with weekly case-based reporting through the Integrated Disease Surveillance and Response (IDSR). It is a vaccine-preventable disease whose surveillance is closely related to that of acute flaccid paralysis (AFP). To eliminate measles, the WHO advises combining enhanced surveillance of the disease and linking the surveillance data to action by increasing the immunization coverage [10].

 

Measles Surveillance in Kenya

 

The Ministry of Health (MOH) adopted a case-based measles surveillance system in 2002 following a measles vaccination campaign and continues to be implemented over the years. The objectives of the surveillance system are to understand the epidemiology of measles cases in order to develop control measures, to predict potential outbreaks and implement vaccination strategies, and to monitor progress toward achieving disease control and elimination goals. At present, Kenya does not have a country-specific measles and rubella strategic plan. Surveillance activities are based on guidelines that were last revised in 2013. This surveillance is conducted through the IDSR and is monitored through process indicators set by WHO.

 

The amendments in the Constitution of Kenya, 2010 introduced two levels of government; the National and 47 County Governments. Following the adoption of the constitution, health was devolved from being a National to a County function in March 2013. This allowed the counties to autonomously manage health operations in accordance with established provisions in the constitution. The health facility levels are classified as Level 6 (National Referral), Level 5 (County Referral), Level 4 (Sub-county Hospital), Level 3 (Health Centre) and Level 2 (Dispensary).

 

Measles case detection starts at the community level using lay case detection that is used by community health promoters (the presence of fever and rash). Case investigation is done through a case investigation form (CIF), the MOH 502 that is completed in triplicate at the facility level. The Sub-County Disease Surveillance Coordinator (SCDSC) is responsible for filling, or cross-checking, the CIFs and sending the collected samples to the laboratory. Measles surveillance at the county level is supervised by the County Disease Surveillance Coordinator (CDSC). A copy of the CIF is retained at the facility, the second retained at the sub-county level while the original accompanies the blood specimen to the Kenya Medical Research Institute (KEMRI) measles laboratory. The laboratory allocates Lab and identification numbers and analyzes the samples for both measles and rubella IgM antibodies. KEMRI maintains a laboratory database for measles samples tested, which is shared with the surveillance department to update the epidemiological database (Figure 1). Aggregate reporting of measles cases from the County to the National level is done through the weekly reporting form, the MOH 505.

 

An outbreak of measles is declared upon the reporting of five (5) suspected cases or the laboratory confirmation of three IgM-positive cases within a facility or sub-county in one month. Subsequently, patients who meet the suspected case definition for measles are line listed, with regular collection of samples to confirm the end of the outbreak. The surveillance department of the measles program receives a weekly line list from the counties on Fridays, which includes both the suspected cases whose samples were sent to the laboratory, and epidemiologically linked cases. Data entry into the Division of Disease Surveillance and Response (DDSR) database is done through Epi Info 3 and stored as a Microsoft (MS) Access database. Ideally, the two databases (epidemiological and laboratory) are harmonized every two weeks to check for data entry errors and merged into a single database. However, harmonization of the two databases is currently conducted bi-annually.

 

For the period January to July 2023, Kenya reported 141 confirmed measles cases in 10 counties with 10 deaths, a case fatality rate of 1.4% [11]. For effective surveillance to achieve the elimination of measles, the targets set by WHO include a national rate of less than one (1) measles case per million population with 80% of the sub-counties expected to investigate at least one or more suspected cases every year, and 95% immunization coverage [12]. With an established surveillance system and regular SIAs since 2002 [13], Kenya should be making strides towards the elimination of measles. We evaluated the measles surveillance system in Kenya to assess its effectiveness and identify gaps in its operation. The descriptive analysis of measles cases provided an understanding of the distribution of measles cases in the country during the evaluation period. The information generated from this study is useful for better planning of measles surveillance and response activities in Kenya and beyond.

 

 

Evaluation design

 

We used the US-CDC guidelines for the evaluation of a public health surveillance system to evaluate the measles surveillance system. We conducted a retrospective review of measles case-based data reported on the IDSR platform from 1st January 2020 to 31st December 2021 from the 47 counties in Kenya. Since measles affects persons of all ages, the population of interest was the entire population of Kenya for the period 2020-2021.

 

Case Definition

 

Lay case definition: is any person with a rash and a fever.

 

Suspected case: is a person with a maculopapular rash and fever; plus one of cough, coryza, or conjunctivitis.

 

Probable case: is a suspected case epidemiologically linked to a confirmed case.

 

Confirmed case: is a suspected or a probable measles case that has been investigated and has serological confirmation of recent virus infection (IgM positive) having not received measles vaccination in the 30 days preceding specimen collection.

 

Discarded/not measles: is a suspected or a probable measles case that has been thoroughly investigated, including the collection of adequate blood specimens, and lacks serological evidence of recent measles virus infection (IgM negative) or is considered to have IgM positivity due to measles vaccination within the 30 days preceding sample collection.

 

Compatible measles: is a suspected case that has not had a blood specimen taken for serologic confirmation and is not linked epidemiologically to any laboratory-confirmed case of measles. Suspected measles cases that have no definite proof of recent infection (IgM test indeterminate repeatedly) may also be classified as compatible.

 

Indicators

 

The key indicators for measles surveillance include an annualized non-measles febrile rash illness (NMFRI) rate (target of ≥2 cases per 100,000 population). This is calculated by dividing the discarded cases of measles by the projected national population and multiplying the answer by 100,000 [14,15]. Other indicators are the proportion of sub-counties reporting one or more rash illness cases per year (target of ≥80% of the sub-counties); the proportion of reported measles cases with blood specimens collected within 30 days of rash onset (target of ≥80% of specimens); transit time of samples to the laboratory (target of ≥80% arriving within 3 days of collection) and turnaround time of results feedback to the counties (target of ≥80% of results sent back within 7 days of arrival of specimen to laboratory). Using the Kenya National Bureau of Statistics population projections for each year, the annual incidence of measles was calculated as cases per million population.

 

Data collection

 

We obtained case-based measles data for the years 2020 and 2021 from the DDSR data manager in the form of MS Access. For the assessment of the surveillance system attributes, we used a semi-structured graded questionnaire with 12 questions to interview five surveillance officers, three clinicians and two laboratory staff. Two of the surveillance staff were from the DDSR and three were from the subcounty level. The clinicians were from Level 3 and Level 4 health facilities while the laboratory officers were from KEMRI. Sampling of participants was purposeful to represent the regions of Kenya (Nairobi Metropolis, Eastern, Western, Rift Valley, Central and Nyanza). Random selection of the officers was done using a list provided at the national and sub-county level. A similar sampling method was used for selecting the health facilities. To assess simplicity, interviewees were asked how easy it was to fill out the CIF. For sensitivity, we asked whether they believed the system was able to detect measles cases in the sub-counties. We assessed the stability of the surveillance system based on the interviewees´ opinions on its capability for continuous operation without failure. Timeliness encompasses the speed or delays between steps in the detection of a measles outbreak and the institution of prevention and control measures. We assessed timeliness using the recommended transit time for samples of within three (3) days and the turnaround time for results within seven (7) days. We also used the time from the declaration of an outbreak to its response to assess for timeliness. Representativeness of the surveillance system was assessed based on its ability to describe the occurrence of measles in time, place and person. During the interviews, the attributes of simplicity, sensitivity, stability, timeliness and representativeness were scored as poor (1), satisfactory (2) and good (3). An attribute is scored poor when deemed dysfunctional, satisfactory when working averagely and good when viewed as fully or near-fully functional. We determined data quality based on the proportion of unknown, inconsistent or blank responses within the dataset. The proportion of missing key variables was calculated using the number of missing variables as the numerator and the total reported measles cases as the denominator. We calculated the predictive value positive (PVP) based on the proportion of reported cases that actually have the health-related condition under surveillance (ie. IgM positive cases). The usefulness of the surveillance system was determined by the public health action taken as a result of information provided by the system.

 

Data analysis

 

We obtained the data in the form of MS Access and transferred them to MS Excel for ease of data cleaning and analysis. A descriptive analysis of the data was done to characterize the measles cases by time, person and place. Proportions for the suspected cases were calculated using the total suspected cases over the two years as the denominator. The same was done for confirmed cases using the total confirmed cases as the denominator The scores for simplicity, sensitivity, stability, timeliness and representativeness were calculated by simple addition from the responses provided by the interviewees. The percentage score of the attribute was calculated by using the sum total from the interviewees as the numerator and the maximum score of 30 as the denominator and multiplying by 100. The transit time of samples to the laboratory was calculated by checking the difference in days between the date the sample was sent and the date it was received at the laboratory. Turnaround time for results was calculated by the difference between the date results were sent back to the county and the date the sample was received at the laboratory. We calculated the predictive value positive of the surveillance system using the total confirmed cases divided by the total samples tested. We assessed the usefulness of the system by the interventions put in place as a result of information provided by the surveillance system. We calculated the surveillance indicators for measles using formulas provided by WHO [16]. The cases with missing requisite variables for the descriptive analysis were omitted during the respective analyses.

 

Ethical considerations

 

Verbal permission to obtain data from the DDSR database was sought from the head of the division. The data were de-identified to maintain anonymity and protect the confidentiality of the patients. Ethical clearance was not sought as this was considered a routine quality improvement public health activity. Verbal consent was obtained from the officers interviewed during the surveillance system evaluation. The data were safely stored in a password-protected computer.

 

 

Surveillance System Evaluation

 

Simplicity

 

The system scored 97% (29/30) in simplicity among those who were interviewed, with nine out of ten interviewees giving it a perfect score. They described the CIF as easy to fill and not time-consuming, but one interviewee reported difficulties filling it and scored it as satisfactory.

 

Sensitivity

 

Seven interviewees reported the system as sensitive enough in detecting measles outbreaks while three scored it as satisfactory, with an overall score of 90% (27/30). The system was able to detect a measles outbreak in five counties in May 2020.

 

Stability

 

The system was described as stable by seven out of the 10 interviewees. Two of the interviewees at the subnational level labelled it unstable owing to the frequent stockouts of the CIFs. Overall, the system scored 80% (24/30) on stability.

 

Timeliness

 

The surveillance system scored 67% (20/30) on timeliness. Three interviewees labelled it timely, four as satisfactory and three as not timely at all. For transit time of samples, we found that 54% (1,126/2,077) of samples arrived in the laboratory within 3 days of its collection against a target of ≥80%, and for the turnaround time for results we found that only 17% (353/2,077) of results were sent back within 7 days of arrival of specimen to laboratory against a ≥80% target. There was a delay of more than a year between the detection of an outbreak in 2020 and the implementation of an SIA in 2021.

 

Data quality

 

Key data variables were missing in the database, including vaccination status (45.16%, 938/2,077), date of onset (0.5%, 10/2,077), the unique identifier (0.5%, 10/2,077) and age (13.0%, 270/2,077). Dates when samples were received at the lab (32%, 666/2,077) and turnaround times (84%, 1,609/1,915) were missing. The final classification was missing in 7.8% (162/2,077) of the suspected measles cases.

 

Predictive value positive

 

The total samples tested for measles were 1,915, out of which 188 were IgM positive. This gave the surveillance system a PVP of 9.8% (188/1,915).

 

Representativeness

 

All 10 interviewees agreed that the measles surveillance system in Kenya was representative, scoring it a perfect 100% (30/30).

 

Usefulness

 

The interviewees described the system as useful as it provided data to detect an outbreak of measles in five counties in May 2020. The public health information generated from these data was used to conduct a risk assessment at the national level, leading to the implementation of a measles SIA in 22 counties in June 2021.

 

Epidemiology of Measles Cases

 

We analyzed a total of 2,077 measles cases reported during 2020 - 2021, with 1,160 (55.8%) of those in 2021. A total of 224 (10.8%) measles cases were reported in November 2021 (Figure 2). In terms of gender, males were 55.7% (1,150/2,077) of the reported cases. The age group 0 - 4 years accounted for 56.3% (1,017/1,807) of the reported cases with the age variable (Table 1.) The vaccination status for 45.2% (938/2,077) of the reported cases was missing (Table 2). Cases reported in West Pokot County were 25.7% (534/2,077) of the total cases.

 

The total laboratory-confirmed cases in Kenya were 188 during 2020 - 2021 from 1915 samples tested, with a positivity rate of 9.8%. In 2021, there were 101 (53.7%) confirmed cases, with 19.7% (37/188) of the confirmed cases in November 2021 (Figure 2). Males contributed 52.7% (99/188) of the confirmed cases. Laboratory-confirmed measles cases were reported in 64% (30/47) of the counties, with 23.4% (44/188) of the confirmed cases being from Garissa County (Figure 3). This represented an attack rate of 52/1,000,000 population (Figure 4). The estimated incidence rate of measles in Kenya was 1.78 per million population in 2020 (87/48,817,537) and 2.03(101/49,720,225) per million population in 2021. The population used is as per projections by the Kenya National Bureau of Statistics.

 

Indicators

 

The annualized NMFRI rate was 1.70 per 100,000 population in 2020 (i.e.(830/48,817,537)X100,000) and 2.1 per 100,000 population in 2021 (i.e. (1,059/49,720,225)X100,000) against a target of ≥2 per 100,000 population per year. The proportion of sub-counties reporting at least one (1) suspected case of measles with a blood specimen collected was 51.0% (148/290) in 2020 and 78.3% (227/290) in 2021, with the target being at least ≥80% of the sub-counties. The proportion of reported measles cases with an accompanying blood specimen to the laboratory within 30 days of rash onset was 100% (917/917) in 2020 and 86% (998/1,160) in 2021 (the target is ≥80%).

 

 

The measles surveillance system in Kenya is viewed as simple by the users, with a CIF that is easy to complete and a clear chain of reporting. A simple surveillance system is likely to increase the reporting rate and, subsequently, the sensitivity of surveillance. It encourages broader participation of healthcare workers, increasing the scope of reporting. Despite frequent stockouts of CIFs and the likely disruption of surveillance activities by the COVID-19 pandemic, the system is viewed as stable by its users. However, there was a decline in the number of measles cases reported during the initial period of COVID-19, consistent with a study done in Brazil [17], likely as a result of prioritization and diversion of health resources towards COVID-19 response. Through its ability to detect an outbreak in May 2020, and provide data that were used for planning and implementation of a measles SIA in June 2021, the system is viewed as both sensitive and useful for outbreak detection and response. While the surveillance system was able to meet the objective of describing the characteristics of measles cases, it has not predicted outbreaks. Vaccination strategies have been developed based on data provided by the surveillance system. The surveillance system was able to show an increase in the annual incidence rate of measles from 2020 to 2021. The system was characterized as being representative; it was able to depict the geographical, spatial and demographic distribution of measles cases in 2020 and 2021.

 

The PVP of the surveillance system for the period under evaluation was 9.8%, which is much lower than the study findings in Ethiopia [18] and Nigeria [19]. The low PVP is possibly a result of the sensitivity of the surveillance system owing to the broad case definition that is designed to detect all measles cases. The timeliness surveillance system was found to be suboptimal owing to the long transit time for samples and turnaround time for results from the laboratory, and the delayed implementation of the measles SIA for more than a year. Timeliness of surveillance is a major challenge in Africa, possibly due to delays occasioned by the reliance on the use of a paper-based as opposed to an electronic system [19] The delay in implementing the measles SIA was likely occasioned by the prioritization of COVID-19 response. Measles SIAs are aimed at interrupting the transmission of the virus, hence delaying immunization is likely to prolong community transmission of measles, especially among those who are most susceptible [20].

 

The data quality of the surveillance system was poor, with omission of key variables such as unique identifiers, age, date of onset, vaccination status and final classification of the suspected cases. This is likely to lead to misrepresentation and poor planning. This is consistent with studies done in Ethiopia [21]. Lack of regular training on surveillance, inadequate support supervision and infrequent data audits are likely to be the main reasons for the poor data quality of the surveillance system.

 

Most measles cases were reported in 2021. This is likely linked to the gradual recovery of the surveillance activities from the effects of the COVID-19 pandemic. The highest number of cases reported were in June (214) and November 2021 (224). During these two months, there was active case finding for measles; during the SIA in June, and the national integrated case finding in November. The most affected age group was 0-4 years, suggesting that measles is more prevalent in those under 5 years of age [4]. Most of the reported measles case were male accounting for 55.7% (1,150/2,077), suggesting that males are at a higher risk of measles. This finding is in contradiction to a study findings in Ethiopia [22]. However, measles has been found to be common in males in studies conducted in Iran [23] and Malaysia [24]. West Pokot County reported the highest number of measles cases 25.7%, 534/2,077), accounting for slightly more than a quarter of the total cases during the study period.

 

A total of 188 cases were confirmed by the laboratory over the two years, with the most cases in November 2021 (37/188). The majority (53.7%) of the laboratory-confirmed cases were in 2021, with male cases being the most at 52.7% (99/188). This finding is consistent with studies conducted in seven countries in 2022 [25] and in Ethiopia [26] that found measles to be slightly more common in males. Garissa County recorded the highest number of cases at 23.4% (44/188), and the highest attack rate over the study period of 52/1,000,000 population. This is possibly due to low immunization coverage owing to the vastness of the county, which is compounded by the increased cross-border movement of refugees from neighbouring Somalia.

 

The analysis of case-based measles surveillance data indicates that Kenya continually detected confirmed measles cases over the study period. Laboratory-confirmed measles cases were reported in more than half of the counties in Kenya. The annual incidence rate of measles in both 2020 and 2021 was above the WHO goal of less than 1 per million population. During the study period, the surveillance system continued to detect measles cases despite the disruption to surveillance activities such as the devolution of the health sector and, most recently, the COVID-19 pandemic. The detection of confirmed measles cases in 30 out of the 47 counties during the evaluation period leads us to believe that measles is endemic in some parts of Kenya. This is consistent with the findings of a study conducted in Ethiopia in 2021 [27]. This finding suggests that WHO-AFRO has not been able to attain measles elimination by the 2020 target set by WHO.

 

With a measles outbreak declared in May 2020 and an SIA conducted in June 2021, there was a delay of more than a year in the initiation of a definitive measles response. Delays in time taken to implement SIAs for measles are common in Kenya [16]. With these delays in response to measles outbreaks, there is the possibility of continued community transmission of the disease making its elimination difficult to achieve.

 

The annualized NMFRI rate in 2020 was 1.70/100,000, below the indicator target of ≥2/100,000 population. The proportion of sub-counties reporting at least one suspected case of measles was low at 51.0%, below the recommended target of 80%. The failure to meet the two indicators is an indicator that there was a general underreporting of suspected measles cases, which could have led to continued community transmission of the virus undetected. This was likely due to the disruption of measles surveillance by the COVID-19 pandemic [28]. This may have been caused by diminished healthcare performance and diversion of resources towards COVID-19 response. The surveillance system was, however, still able to detect measles cases. Subsequently, a measles outbreak was declared in May 2020 using information provided by the system.

 

Limitations of the study

 

A key limitation of this study was the lack of a gold standard public health surveillance system for comparison. To mitigate this, we used the US-CDC guidelines for the evaluation of a surveillance system as a reference point for the exercise. Another limitation of this study was the missing data variables, especially the date of onset and age. We mitigated this by conducting analysis for measles cases where the required variables were present. This is likely to cause bias and misrepresentation of the descriptive epidemiology of measles in Kenya during the study. Due to financial constraints and limited time to conduct the study, we were only able to interview ten (10) users of the surveillance system. We relied on the opinion of the interviewees to determine the sensitivity of the surveillance system instead of computing it using the laboratory results, which may have introduced a reporting bias.

 

 

The surveillance system can be relied upon to continue operating without failure, such as was the case during the peak of the COVID-19 pandemic. The simplicity of the surveillance system, coupled with its stability, increases the ability of the system to promptly detect measles cases and outbreaks at the sub-national level. Despite a suboptimal performance, the measles surveillance system is still useful in detecting measles outbreaks in the country. Delays in responding to measles outbreaks and the implementation of mitigation measures such as SIAs are hampering progress towards measles elimination in Kenya. There is evidence of endemic measles transmission in Kenya, with the country not closer to elimination of the disease. The increase in the annual incidence rate of measles from 2020 to 2021 is worrying as it is apparent that Kenya is not closer to measles elimination.

 

We recommend the allocation of more resources to measles surveillance activities, including training of surveillance and clinical staff at the national and subnational levels. This is aimed and enhancing early detection of measles outbreaks. There should be a timely and effective response to measles outbreaks, avoiding the long delays that have been experienced in the past. Data quality audits of the surveillance system should be conducted frequently to ensure that information provided is correct and actionable. Finally, a country-level measles-rubella strategic plan should be developed to guide efforts towards elimination of measles in Kenya.

 

 

The authors declare no competing interests.

 

 

FMN and CN participated in the conceptualization and design of the study. DK and HL provided the surveillance data for the study period, and RN provided the laboratory data. FMN conducted the data analysis. FMN, CN, DK and HL interpreted the results of the study. FMN drafted the manuscript. HL and DK reviewed the manuscript. FMN, HL and DK edited the manuscript. All the authors reviewed and approved the final draft of the manuscript.

 

 

The authors would like to extend much appreciation to the Ministry of Health, Kenya, the Kenya Field Epidemiology and Laboratory Training Program, and the KEMRI Measles Laboratory for providing the resources to facilitate this study.

 

 

Table 1: Distribution of Measles Cases in Kenya, 2020 - 2021

Table 2: Vaccination status of the reported measles cases in Kenya, 2020 -2021

Figure 1: The measles surveillance system in Kenya

Figure 2: Epi-curve of reported measles cases in Kenya, 2020 - 2021

Figure 3: Confirmed measles cases by county in Kenya, 2020 - 2021

Figure 4: Measles attack rate by county in Kenya, 2020 - 2021

 

 

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