The Context of Hypertension Management in India: A National Assessment of the Potential for Digital Technology Interventions in the Public Health Care System

Shivani A Patel (  s.a.patel@emory.edu ) Emory University https://orcid.org/0000-0003-0082-5857 Kushagra Vashist Emory University School of Public Health Prashant Jarhyan Public Health Foundation of India Hanspria Sharma All India Institute of Medical Sciences Priti Gupta Centre for Chronic Disease Control Devraj Jindal Centre for Chronic Disease Control Nikhil Srinivasapura Venkateshmurthy Public Health Foundation of India Lisa Pfadenhauer Emory University School of Public Health Sailesh Mohan Public Health Foundation of India Nikhil Tandon All India Institute of Medical Sciences


Introduction
High blood pressure is responsible for over 6.08 million deaths across low-and middle-income countries (LMICs) annually. 1 Timely diagnosis 2 and appropriate medical management 3 of high blood pressure are foundational evidence-based interventions to mitigate resulting poor health outcomes. 2 Digital technologies are being increasingly leveraged as a potential implementation strategy to widely deliver these evidence-based interventions at scale. 4 Examples of technologies include combinations of mobile health (mHealth) applications to facilitate community screening for blood pressure, 5 SMS messaging to communicate with patients, 6 usage of electronic medical records to track and manage patients requiring chronic care, 7 and use of electronic decision support systems to assure adoption of guidelines-based care. 8 To achieve sustainable impacts on population health outcomes, the World Health Organization has called on member states to explore "how digital technologies could be integrated into existing health systems infrastructures and regulation, to reinforce national and global health priorities." 9 In India, the national government has invested in developing technological platforms to improve care for hypertension and other noncommunicable diseases (NCDs) through its community-based NCDs Prevention, Screening, Control and Management Initiative under India's Comprehensive Primary Health Care Program. 10,11 Such government-sector initiatives are particularly important in rural settings, which have experienced steady increases in cardiovascular disease and other chronic comorbidities associated with high blood pressure 12 but whose healthcare infrastructure has historically focused on delivering care for maternal and child health as well as infectious disease control.
As with any complex intervention, digital technology strategies must be adapted for the local context, or contextual modi cations are needed in order achieve similar effects as in previous contexts. 13,14 At a minimum, population-level management of high blood pressure with or without technology requires a combination of the essential drugs and adequate healthcare personnel for appropriate administration of drugs. 15 Operationally, the responsibility to deliver healthcare and maintain healthcare infrastructure has been vested with districts, 15 administrative units within states. Contextual characteristics of healthcare facilities and the broader community at a district-level therefore may critically inform strengths and weaknesses in the current healthcare system as the nation moves towards scale up of hypertension care through technology-assisted approaches.
Several implementation science frameworks exist to describe and evaluate dimensions of context, variously de ned. [16][17][18] We employ the Context and Implementation of Complex Interventions (CICI) framework 18 to evaluate the current healthcare infrastructure context to support digital health interventions for high blood pressure diagnosis and management in India. We further examine dimensions of epidemiologic, socio-economic, and geographical context to assess the broader contextual correlates of healthcare infrastructure to support digital health technologies.

Data sources
The fourth round of the District Level Household and Facility Survey (DLHS-4) is the most comprehensive and latest nationwide health facility assessment conducted by the government of India with coverage across 26 states and 3 union territories with detailed districtlevel data. The survey was conducted between 2012-2014. Facility data were collected through four separate modules (including questionnaire, physical inspection, and assessing registers) designed for each facility type. Facility data were available for 1,540 District and Sub-District Hospitals (DH), 4,810 Community Health Centers (CHCs), 8,540 Primary Health Centers (PHCs), and 18,367 Sub Centers (SCs). In 377 districts, we analyzed DH facilities meeting the Indian Public Health Standards (IPHS) minimum bed criterion of 101 beds; in 171 districts where no facility met this criterion, we analyzed sub-divisional/sub-district hospitals (minimum of 31 beds). Data from two states (Gujarat and Jammu and Kashmir) and four union territories (Dadra and Nagar Haveli, Daman and Diu, Delhi and Lakshadweep) were not available.
To characterize broader community context at the district-level, we combined data from the household survey of the DLHS-4, the Annual Health Survey (AHS), 19-21 and the fourth round of the National Family Health Survey (NFHS-4). 22 The DLHS-4 household survey was conducted in 2012-14 in all states and union territories in India except the for the states of Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha, Rajasthan, Uttar Pradesh, Uttarakhand and Assam. States that were not covered in the DLHS household survey were instead covered through the government's AHS, conducted in 2012 − 13. 21 Together, the DLHS-4 and AHS provide coverage of districts across all Indian states in 2012-2014. NFHS-4 was conducted in 2015 − 16 22 and provides data and households with district linkages.
NFHS employed a multi-stage strati ed sampling scheme and were designed to be representative at the state and national levels. Strata were de ned by urban-rural setting and the primary sampling units (PSU) were villages in the rural stratum and wards in the urban stratum. NFHS provides data for all of the districts under study, with the advantage of having identical measures across all districts.

Contextual indicators
The CICI framework was developed to provide guidance on the interacting dimensions of context, implementation, and settings that may impact the successful delivery of complex interventions. 18 Critically, the framework lls the gap in how upstream contextual factors beyond the organizational context may impact the implementation of a complex intervention with community-facing components. CICI considers 7 domains of context-geographical, epidemiological, socio-cultural, socio-economic, ethical, legal, political. Here, we investigate the interplay among the CICI described political (healthcare infrastructure), epidemiological (measures of blood pressure, body mass index, older population), socioeconomic (aggregate community wealth), and geographical (region, urbanicity) domains. Although socio-cultural, ethical, and legal aspects warrant consideration, they are not easily quanti able and are not included in the present study.

Healthcare infrastructure
The CICI places healthcare infrastructure within the political domain because it is dependent on healthcare nancing and regulations. As healthcare infrastructure is the sine qua non of medical management of blood pressure, this is the focal domain of this study. The public healthcare system in India is organized as a hierarchy of four tiers of facilities that include: sub-centres at the village-level (SC; lowest levels of skilled personnel and resources; tasked with blood pressure screening), primary health centres that serve several villages (PHCs; contain a physician; tasked with blood pressure diagnosis and providing basic medical treatment), community health centres that serve the administrative unit known as a block (CHCs; include NCD clinics and are central to the integration of NCD care into primary care), and district hospitals that serve an entire district (DH; include higher levels of trained personnel and more sophisticated infrastructure).
This model was designed to broaden coverage within existing resources and with the aim of progressive referral from lower to higher levels of health care depending of the need of the individual patient and the availability of resources (skilled human resources, infrastructure and services).
Healthcare system infrastructure indicators were developed based on a combination of the Indian Public Health Standards (2012 revision). 23 We used the DLHS facility data to separately quantify the availability of diagnostics (blood pressure instrument), antihypertensive medication, potential sta ng (medical o cer, staff nurse, pharmacist, and community health worker), and speci c infrastructure needed to support digital health initiatives (power supply, computer, internet connection). Indicators were contingent on the facility tier (DH, CHC, PHC, SCs). For example, at the district hospital level, a medical o cer, public health nurse, and a pharmacist are designated as essential staff, whereas in health sub-centres (lowest level), only a female health worker is designated as essential. We only included personnel who, from our experience, are likely to be involved in hypertension care. The availability of indicators across tier of the healthcare system hierarchy is described in Supplemental Table 1.

Epidemiologic, socioeconomic and geographical context
To assess the alignment of healthcare infrastructure with broader contextual characteristics, we also considered measures of the epidemiologic, socioeconomic, and geographical context. Such contextual indicators may have a bearing on allocation of health system resources. For example, health system infrastructure relevant to blood pressure may be directed to communities with greater epidemiologic burden of hypertension and its risk factors. Alternatively, it may be the case that wealthier communities may be the recipients of larger public investments in health.
Districts were characterized for epidemiologic and geographical context by aggregating household and individual data from the combined DLHS and AHS datasets. For epidemiologic indicators, we computed the mean systolic blood pressure and the mean body mass index by district. Geographical context was de ned by region as well as the proportion of the population that resides in an urban area. Districts were classi ed as belonging to one of six regions-north (RJ, UT, HR, PJ, HP, CH), west (MH, GA), south (TN, AP, AN, KL, KA, PY), central (CG, MP, UP), east (BR, JH, OR, WB) and northeast (AP, AS, MN, MZ, ML, NL, SK, TR)-according to the Indian Census. For socioeconomic context, we assigned each district the mean of measure of household wealth, a standardized asset-based measure of relative household wealth (mean = 0 and SD = 1) computed separately for urban and rural households. District-level wealth scores were derived from NFHS-4, for a measure of relative wealth comparable across all districts.

Statistical analysis
Districts were the unit of analysis for this study. All statistical analyses were conducted in SAS v9.4 (SAS Institute; Cary, NC). Missing data was assumed to indicate the absence of that the factor; for example, facilities lacking missing data on internet availability were assumed to have no internet.
District infrastructure indicators were rst computed for all health facilities separately. Health facilities were scored 1 for the presence or 0 for the absence of each indicator available for its facility type. A composite variable was created for each facility and was coded as 1 if a facility reporting having all required indicators (diagnostics, medication, potential sta ng, and IT infrastructure). District-level infrastructure scores were computed as the mean of health facility context indicators for all facilities within a district. For example, the mean of the overall CHC facility composite score re ects the proportion of CHCs in a particular district that met all of the infrastructure criteria listed above.
We conducted a descriptive analysis of all indicators and composite scores and estimated means and 95% con dence intervals (CIs) for the nation and by region. We computed the Spearman correlation among all of the contextual indicators, including the mean composite healthcare system infrastructure availability, at the district level. We further conducted logistic regression analysis to estimate the association between epidemiologic and geographical context (exposures) and the composite measure of healthcare system potential to support digital health interventions for blood pressure management (outcome) at the facility level. Models were implemented using generalized estimating equations to account for clustering of districts within states and were estimated separately for each facility tier (i.e., DH, CHC, PHC, and SC).
Finally, because the DLHS-4 was elded in 2012, we conducted a sensitivity analysis to assess the correlation between total sta ng of essential personnel at CHCs in 2012 and 2018 at the state level. Due to data limitations, only state level comparisons of sta ng were possible and we focus on the CHC due to its emphasized role in NCD care.

Results
Data from a total of 18,344 SCs (lowest tier facility), 8,526 PHCs, 4,807 CHCs, and 548 DHs (highest tier facility) across 548 districts covering 29 states and union territories were analyzed. Figure 1 shows healthcare infrastructure by domain and facility tier. Among DHs, only 61% possessed all staff deemed minimally su cient and essential. On the other hand, 92% had supporting IT infrastructure (internet availability was the only indicator surveyed at this level). Considering both essential staff and IT infrastructure, 57% of DHs were positioned to incorporate digital technologies for blood pressure management. Note that DHs were not surveyed for diagnostics and medications, ostensibly because these are presumed present within this facility. Among CHCs, 98% had BP instruments and 87% had antihypertensive medication. However, only 50% of CHCs possessed essential staff and IT infrastructure (regular power supply, facility computer, and working internet connection). Taking all of these elements together, 25% of CHCs were ready to undertake IT based interventions. PHCs showed a pattern similar to CHCs, with lower sta ng and IT infrastructure. 96% of PHCs had BP instruments and 75% had some form of antihypertensive medication. However, only 15% had all essential staff positions lled, and only 28% had the IT infrastructure (regular power supply, facility computer, and working internet connection) expected at this level. Less than 5% of PHCs were ready for IT interventions for BP, the lowest readiness across facility type. Finally, SCs had BP instruments and essential staff (female health workers) in 94% and 88% of facilities, respectively. IT infrastructure (power supply) was relatively low at 30%. Overall, 23% of SCs were ready to undertake IT interventions for blood pressure. Figure 2 shows the distribution of IT infrastructure availability, de ned as a composite of all IT indicators available, by healthcare facility tier and region in 2012-2014. In general, we observed that IT infrastructure availability was highest for DHs and lower for CHCs, PHCs, and SCs. By region, districts in northeastern and central India tended towards having the least prepared health facilities with respect to IT infrastructure. The largest regional variability in infrastructure was observed at the level of CHCs and PHCs, where IT infrastructure availability in the south and west statistically signi cantly exceeded the national mean while infrastructure for districts in central and northeast India was below the national mean. Table 1 shows the correlation between the mean district composite infrastructure score and contextual indicators at the district level. DH infrastructure composite score was not correlated with the composite score of other facility tiers in the district, but was inversely correlated with average systolic blood pressure and positively correlated with the proportion of the population aged 60 years and older as well as the mean household wealth index. There were strong correlations among CHCs, PHC, and SC composite infrastructure, with correlations ranging from ranging from .29 − .44). CHC, PHC, and SC composite infrastructure were also positively and signi cantly correlated with community-based average systolic blood pressure and body mass index, the proportion of urban residents, and aggregate household wealth. The proportion of the population aged 60 years and older was not signi cantly associated with CHC or SC infrastructure.  Table 2 shows the association of epidemiologic, socio-economic, and geographic contextual indicators with district-level composite infrastructure measures from adjusted logistic regression models accounting for all other context characteristics. Among DHs, the population aged over 60 y was signi cantly and positively associated with DH infrastructure (adjusted OR[aOR] = 1.74; 95%CI: 1.05-2.90). Among CHCs, location in a Western state, mean BMI, mean SBP, and percent of the population that was urban were all positively and statistically signi cantly related to the composite infrastructure score, suggesting that the availability of CHC infrastructure was aligned with populations with higher levels of blood pressure at the time of survey. Similar associations were seen in adjusted models among PHCs, but household wealth was the only community factor statistically signi cant in the adjusted model (aOR = 5.89; 1.41-24.59). PHCs in the southern and western regions were much more likely than those in the central region to possess all the infrastructure elements needed for digital health technologies in BP management. The regional pattern was also apparent for SCs, which were more likely to have better infrastructure in the west. Population BMI and urbanicity, but not wealth, were associated with more favorable SC infrastructure.

Discussion
We identi ed and quanti ed several constraints to implementing digital health interventions for blood pressure management within the government sector in India in 2012-2014. Our examination revealed that shortfalls in essential staff may be a larger barrier to these programs than the availability of IT-speci c infrastructure. We also observed that gaps were generally larger for lower tier facilities and for facilities in northeastern and central India, and substantial variation by region of the nation. As a potential strength, we noted that the availability of all healthcare system infrastructural elements (diagnostics, medications, staff, and IT infrastructure) tended to be aligned with the location of higher need for blood pressure management: districts with higher average SBP and BMI were more likely to meet the composite infrastructure criteria. These data provide early benchmarks for state planning and allocation for resources at the district level planning.

Strengths and limitations
A major strength of our study was the use of the CICI framework, which provided a conceptual lens which informed our analysis. While were not able to incorporate the legal, political, or socio-cultural context into the analysis, the ndings must be interpreted against the backdrop of these contextual domains. Since the DLHS-4, there has been major public investment in strengthening care for priority NCDs such as hypertension. To mobilize awareness of priority NCDs among communities for whom there is low culture of engagement with formal preventative healthcare, the Ministry of Health & Family Welfare, Government of India, published operational guidelines in 2017 to promote universal community screening of hypertension, diabetes, and three common cancers among all adults over the age of 30 years. To identify previously undiagnosed cases of hypertension and identify high risk adults, Accredited Social Health Activists (ASHAs), are lay community health workers incentivized by the government to support auxiliary nurse midwives and other formal healthcare providers through door-to-door screening of adults. From the ASHA to the staff nurse to the medical o cer, digital applications to record and track patient outcomes and provide referral and management prompts are now available. These digital health tools require trained users, along with appropriate blood pressure screening devices, anti-hypertensive medications, and IT-supportive infrastructure such as power supply and internet.
While we used the most recent data available, the facility assessments occurred in 2012-2014, prior to the initiation of many of the digital health initiatives in play today. The nding that human resources may be a larger limitation than the lack of IT capacity or other supplies may no longer hold true due to subsequent investments in this area. Furthermore, our ndings only apply to the government healthcare sector. Many Indians report not utilizing government healthcare due to perceptions of including poor quality of care, doctor unavailability, drug unavailability, absence of healthcare personnel and lack of adequate infrastructure. 24 Analyses such as ours provide quanti cation of speci c gaps within the government sector but do not address capacities (or de cits) in the private sector.

Conclusions
As India and other nations deploy plans to incorporate digital health technologies, our study provides a model for assessment of preimplementation barriers that can be used to advocate for health systems strengthening. Such empirical investigation may challenge preconceived notions regarding the gaps in implementing novel intervention delivery strategies, such as digital technologies. A structural perspective on the environment around incorporating novel technologies into the delivery of care for hypertension and other chronic disease may accelerate our ability to identify and address gaps in the healthcare system and beyond.

Declarations
Ethics approval and consent to participate This analysis focuses on facility-level data which were publicly available. Therefore, no participant consent was sough.

Consent for publication
Not applicable Availability of data and material The data used in this study are publicly available. Both the District Level Health Survey and the National Family Health Survey data are available from the International Institute of Population Sciences, India (http://www.iipsindia.org).  Table 1 for a description of indicators with each category by facility type.