Aim and study design: The aim of the study was to estimate the disease burden of pediatric HIV among children in ‘A’ category district of a high HIV prevalence state, using a multipronged approach
Study setting: The study comprised of three distinct strategies including active surveillance, inclusion of public and private healthcare facilities, data collection from blood banks and NGOs and children attending general and specialized clinics for a better estimation of Pediatric burden. Information regarding study setting and design and sample size estimation have been previously described14. Briefly, District Belgaum was selected after a baseline assessment of three high prevalence districts in South India. Belgaum was chosen for the study as it had a high adult HIV prevalence of 1.43% in 201115. Besides, the coverage of antenatal care and prevention of mother to child transmission (PMTCT) services was high and the district administration was supportive of the study. A mapping of all 971existing (Govt. & private) health care facilities (HCFs) in the district was conducted (ref published report). A total of 285 HCFs from ten talukas with stand-alone and reporting HIV testing facilities (149 Govt. and 136 private) were included in the study.
In 2011, Belgaum district had a total population of 4779661 (Males: 50.7%, Females: 49.3%), 75% of whom were rural and 73.5% (Males: 82.2%; Females: 64.6%) were literate. Strategy 1 used a prospective cohort design to measure the incidence rate of HIV by early case detection in infants and young children (0-22 months) born to a HIV positive pregnant woman registered at one of the public or private health care facilities of the district. The study team visited the identified HCF twice in a week. A crude line list was prepared from the secondary data collected from the HCFs, the list was refined by applying eligibility and removing duplicates. All HIV infected pregnant women residing in any of the 10 talukas, who consented for age –specific HIV blood test for their infants were eligible for enrolment. The women were contacted over phone and visited physically at home or elsewhere (if home visit was not permitted). Pregnant women were followed through their pregnancy & delivery, visited once in a month until delivery and afterwards till their infant was 22 months old. A mother and infant form was filled for each mother- infant dyad. Age-appropriate early testing in infants using DNA PCR dry blood spot (DBS) was conducted at 6-10 weeks, 6-9 months and antibody based ELISA tests at 18-22 months. Demographic information, mother –infant form and a pregnant Positive Women line-list was maintained. Each mother and infant Dyad was assigned a unique identification number.
Strategies II and III used a cross-sectional design. Strategy II aimed to detect HIV infection among children (0-14 years) by family screening of HIV positive parent(s) (PLHAs) referred from ICTC centers, blood banks and community based NGOs in all 10 talukas. If a positive male was detected his wife and children were tested, if a positive female was detected her husband and children were tested. Any HIV infected man/woman, of age 18-49 years, having a biological child of 0-14 years residing in any taluka of Belgaum, who consented for testing of their spouse and children for HIV were eligible and included in strategy II of the study. Public and private HCFs were visited twice a week by the designated teams to check information about HIV infected individuals 18-49 years, they were contacted to find if they had any biological children 0-14 years. The spouse and all children of the positive person (male or female) were subjected to age appropriate HIV testing. Demographic details, data on testing were recorded. A unique identification number for the positive persons identified through family screening was created. The strategy III used screening of sick children visiting health care facilities, in four talukas of Belgaum that included 10 Health care facilities selected using stratified random sampling on the basis of MTCT prevalence, government and non-government, and by levels of health care offered. IMCI -HIV criteria (applicable to 0-5 years age) was adapted by Indian experts to include children >5 to 14 years into the algorithm. Sick children (0-14 years) presenting with suspected signs and symptoms satisfying the ‘Modified Integrated Algorithm’ (including sign symptoms from the IAMI and ‘special clues’) 16 were tested at health care facilities by age appropriate HIV tests. Health care providers from the four participating talukas were trained in the use of the algorithm, operational definitions were developed for each criteria included in the algorithm. The facilities were visited weekly by the research team to find information about children screened positive, referred & tested. Tracking for test results was done through the unique identification number maintaining confidentiality. The strategies (I, II, & III) were expected to comb the district with pediatric cases hidden within families and presenting as masked sicknesses yet unidentified as HIV)
Estimates derived from each of the strategies (1, II & III) were multiplied by an inflation factor derived in a workshop of investigators and the Project Advisory Group/subject experts. The results from the study were extrapolated to the population characteristics within the district, including total population (adult and child), estimated overall adult HIV prevalence, estimated prevalence among pregnant women and reported coverage of HIV testing among the antenatal sub-population. The outline of the study design is depicted in Figure 1.
Figure 1: Outline of the research study
The ethics approval for the study was obtained from the Institutional Ethics Committee of St. John’s Medical College, Bangalore, and regulatory approvals were obtained from NACO, the Karnataka State AIDS Prevention Society (KSAPS) and District AIDS Prevention and Control Unit (DAPCU, Belgaum district.
Study implementation: The ten talukas in the district were divided into three clusters, each of which had five Field Investigators (FIs) and a supervisor (Senior Research Fellow, SRF). In each cluster, a team of one male and one female FI was given charge of two talukas each (strategy 1 & 2), and one additional FI was allotted for the strategy 3 related work. 14 FIs were recruited and trained over two periods, for a total duration of 10 days in technical skills as well as soft skills of maintaining confidentiality& gender sensitive approach. The overall field study was supervised by a medically qualified professional. SRFs planned daily visits of FIs, validated 5% of data and verified forms for accuracy and completion.
Data Management & statistical analysis: Data was double entered using Microsoft Access, cleaned and verified for consistency and analyzed using SPSS version 22.0 and STATA version 13.0
A Project Advisory Group (PAG) guided the study team to develop a Statistical Analysis Plan (SAP). The primary outcome in strategy 1 was cumulative incidence, calculated as the number of new infections per total number of children at risk. A child was at risk till first positive result by any test at any age, by age appropriate testing. For censored observations, time was the duration of follow up. The SAP considered the limitations in coverage of services and response rates, and guided to determine the ‘Net inflation factor’ for each of the 3 strategies. Under strategy I the net inflation factor was derived using the estimated number of pregnancies in the district, proportion of un-tested pregnancies, pregnant mothers not enrolled and un-tested children.. In strategy II and III prevalence of HIV infection among children 0-14 years of age was calculated. Data was analyzed as per the SAP. It was based on the actual/projected 18-49 year population in the study period, estimated number of 0-14 year children, proportion of eligible index persons not recruited and not proportion of eligible children not tested. In strategy III the factors considered for Net Inflation factor were: actual/projected 0–14 year population during the study period, estimated number of 0–14 year children experiencing any morbidity, estimated morbid children reaching a HCF for care, estimated children satisfying screening algorithm, and suitable inflation factors for geographical and institutional factors, morbid children not reaching selected HCF but reaching other facilities in the district, un-screened, non-enrolled and untested children. The estimates derived under the strategies were then multiplied with the inflation factor to come up with the overall estimate. The steps followed are described along with the results section.