A Model of Care Optimized for Marginalized Remote Population Unravels Migration Pattern in India

Abstract

Background and aims: Access to basic health needs remains a challenge for most of world's population. In this study, we developed a care model for preventive and disease-specific health care for an extremely remote and marginalized population in Arunachal Pradesh, the northeasternmost state of India.

Approach and results: We performed patient screenings, performed interviews, and obtained blood samples in remote villages of Arunachal Pradesh through a tablet-based data collection application, which was later synced to a cloud database for storage. Positive cases of hepatitis B virus (HBV) were confirmed and genotyped in our central laboratory. The blood tests performed included liver function tests, HBV serologies, and HBV genotyping. HBV vaccination was provided as appropriate. A total of 11,818 participants were interviewed, 11,572 samples collected, and 5,176 participants vaccinated from the 5 westernmost districts in Arunachal Pradesh. The overall hepatitis B surface antigen (HBsAg) prevalence was found to be 3.6% (n = 419). In total, 34.6% were hepatitis B e antigen positive (n = 145) and 25.5% had HBV DNA levels greater than 20,000 IU/mL (n = 107). Genotypic analysis showed that many patients were infected with HBV C/D recombinants. Certain tribes showed high seroprevalence, with rates of 9.8% and 6.3% in the Miji and Nishi tribes, respectively. The prevalence of HBsAg in individuals who reported medical injections was 3.5%, lower than the overall prevalence of HBV.

Conclusions: Our unique, simplistic model of care was able to link a highly resource-limited population to screening, preventive vaccination, follow-up therapeutic care, and molecular epidemiology to define the migratory nature of the population and disease using an electronic platform. This model of care can be applied to other similar settings globally.

FIG. 1.

(A) Map of operation dynamics. Field teams, which typically consisted of two field workers and a phlebotomist, would travel to various field sites in Arunachal Pradesh. Application development, monitoring of data integrity, and technical support were provided from Maryland. Participant data was uploaded and maintained in the cloud through a secure server. (B) General workflow for field team operations in Arunachal Pradesh. After completing interviews with participants and collecting blood samples through tablet application, basic serologies and liver function testing were completed in Tezpur (field headquarters). Confirmatory testing was done in Kolkata (central headquarters) in addition to further analysis on HBsAg-positive samples. The field team would communicate with collaborators in Maryland for any required adjustments to the tablet application before embarking on their next field trip.

FIG. 1.

(A) Map of operation dynamics. Field teams, which typically consisted of two field workers and a phlebotomist, would travel to various field sites in Arunachal Pradesh. Application development, monitoring of data integrity, and technical support were provided from Maryland. Participant data was uploaded and maintained in the cloud through a secure server. (B) General workflow for field team operations in Arunachal Pradesh. After completing interviews with participants and collecting blood samples through tablet application, basic serologies and liver function testing were completed in Tezpur (field headquarters). Confirmatory testing was done in Kolkata (central headquarters) in addition to further analysis on HBsAg-positive samples. The field team would communicate with collaborators in Maryland for any required adjustments to the tablet application before embarking on their next field trip.

FIG. 2.

(A) Kiran app login screen, activity screen, and participant selection screen. (B) Field user can use these three screens to enter participant answers to questions about demographics and social history. (C) Each participant is identified by a unique barcode, which is scannable by the tablet and done before phlebotomy. Participant can opt out of phlebotomy if they are not interested or not able to have their blood drawn. When eligible, patient can receive HBV vaccination. (D) Web application dashboard. From field and central headquarters, user has the ability to view patient data, look at all microbiology reports, and generate new bar codes for future participants.

FIG. 2.

(A) Kiran app login screen, activity screen, and participant selection screen. (B) Field user can use these three screens to enter participant answers to questions about demographics and social history. (C) Each participant is identified by a unique barcode, which is scannable by the tablet and done before phlebotomy. Participant can opt out of phlebotomy if they are not interested or not able to have their blood drawn. When eligible, patient can receive HBV vaccination. (D) Web application dashboard. From field and central headquarters, user has the ability to view patient data, look at all microbiology reports, and generate new bar codes for future participants.

FIG. 2.

(A) Kiran app login screen, activity screen, and participant selection screen. (B) Field user can use these three screens to enter participant answers to questions about demographics and social history. (C) Each participant is identified by a unique barcode, which is scannable by the tablet and done before phlebotomy. Participant can opt out of phlebotomy if they are not interested or not able to have their blood drawn. When eligible, patient can receive HBV vaccination. (D) Web application dashboard. From field and central headquarters, user has the ability to view patient data, look at all microbiology reports, and generate new bar codes for future participants.

FIG. 2.

(A) Kiran app login screen, activity screen, and participant selection screen. (B) Field user can use these three screens to enter participant answers to questions about demographics and social history. (C) Each participant is identified by a unique barcode, which is scannable by the tablet and done before phlebotomy. Participant can opt out of phlebotomy if they are not interested or not able to have their blood drawn. When eligible, patient can receive HBV vaccination. (D) Web application dashboard. From field and central headquarters, user has the ability to view patient data, look at all microbiology reports, and generate new bar codes for future participants.

FIG. 3.

Phylogenetic analysis of HBV isolates for genotype and subgenotype determination. (A) Phylogenetic analysis of complete preS/S sequences of 43 HBV isolates, along with reference sequences of HBV strains belonging to different genotypes (A to J) derived from GenBank. (B) Phylogenetic analysis of complete preS/S sequences of 16 HBV isolates of genotypes C and D, along with reference sequences of HBV strains belonging to different subgenotypes (C and D) derived from GenBank. Phylogenetic tree was constructed using the Kimura 2 parameter model and neighbor-joining method by MEGA software version 5, and bootstrap resampling was carried out 5,000 times. The Tezpur sequences are indicated by respective isolate numbers beginning with “Kiran.”

FIG. 3.

Phylogenetic analysis of HBV isolates for genotype and subgenotype determination. (A) Phylogenetic analysis of complete preS/S sequences of 43 HBV isolates, along with reference sequences of HBV strains belonging to different genotypes (A to J) derived from GenBank. (B) Phylogenetic analysis of complete preS/S sequences of 16 HBV isolates of genotypes C and D, along with reference sequences of HBV strains belonging to different subgenotypes (C and D) derived from GenBank. Phylogenetic tree was constructed using the Kimura 2 parameter model and neighbor-joining method by MEGA software version 5, and bootstrap resampling was carried out 5,000 times. The Tezpur sequences are indicated by respective isolate numbers beginning with “Kiran.”

FIG. 4.

HBV genome recombination analysis using jpHMM approach. (A) Schematic diagram of jpHMM results of six representative isolates showing the circular HBV genome with alternate genotype D (yellow) and genotype C (green) regions. (B) Representative schematic and blown-up image of one circular HBV C/D recombinant genotype (KRN753). PreC, pre-core.

FIG. 4.

HBV genome recombination analysis using jpHMM approach. (A) Schematic diagram of jpHMM results of six representative isolates showing the circular HBV genome with alternate genotype D (yellow) and genotype C (green) regions. (B) Representative schematic and blown-up image of one circular HBV C/D recombinant genotype (KRN753). PreC, pre-core.