Abstract
Background: District-level population surveys and routine health-management data provide different perspectives on cardiometabolic burden and reported healthcare activity. This study examined whether district-level NFHS-5 cardiometabolic indicators were associated with HMIS-reported diabetes and hypertension outpatient counts in Gujarat, India.
Methods: An ecological analysis included all 33 Gujarat districts. NFHS-5 data were collected in 2019 and HMIS data covered financial year 2021–2022; analyses were conducted in 2026. An exploratory equal-weighted index combined district diabetes burden, hypertension burden, and women’s overweight/obesity prevalence. The primary outcome was the annual public-facility diabetes-plus-hypertension outpatient count. Adult-population denominators and unique-patient identifiers were unavailable, so repeated encounters could not be distinguished and HMIS values were analyzed as absolute reported counts. Spearman correlations, multiplicity-adjusted tests, OLS regression, population-size sensitivity analyses, and spatial-error models were used.
Results: Cardiometabolic burden was positively correlated with absolute HMIS counts (ρ=0.558; Holm-adjusted P=.00498). The unadjusted regression association was positive (β=0.606; P=.00108), but attenuated after adjustment for education, health insurance, and infrastructure (β=0.158; P=.521). When counts were divided by Census 2011 population, the correlation weakened (ρ=0.144; P=.425); spatial-error modeling likewise produced a null adjusted burden association (β=0.044; P=.817). Diabetes burden showed a positive rank association that did not survive Holm correction (P=.124), whereas the diabetes-specific regression remained significant after correction (Holm-adjusted P=.00450). Hypertension-specific associations were not evident.
Conclusion: The study did not demonstrate an independent adjusted association between the exploratory cardiometabolic burden index and HMIS-reported outpatient counts. Absolute HMIS counts should not be interpreted as population-based utilization, coverage, quality, disease control, or unmet need.

Key words: Noncommunicable Diseases; Diabetes Mellitus; Hypertension; Health Information Systems; Ecological Studies; India; Outpatients; Public Health Surveillance