Causal impact of insurance on cardiometabolic risk

The central objective is to estimate whether expansion of insurance coverage causally lowers cardiometabolic risk factor levels in India. Such a question is well suited to quasi-experimental strategies that approximate randomized exposure to coverage using administrative rules or temporal and geographic variation exogenous to individual health trajectories. The approach allows the analysis to address confounding by socioeconomic status, health-seeking behavior, and regional care capacity, all of which correlate with both insurance take-up and cardiometabolic outcomes.

The evaluation proceeds by defining exposure to coverage using policy eligibility criteria and rollout timing, measuring outcomes with biomarker and anthropometric indicators, and estimating effects under a hierarchy of designs. Primary estimands include the average intention-to-treat (ITT) effect of eligibility on risk factor levels and the local average treatment effect (LATE) on those induced to enroll, obtained via two-stage least squares where eligibility instruments actual coverage. Precision is characterized by 95% confidence intervals, and robustness is interrogated through falsification tests, alternative specifications, and sensitivity to clustering and weighting choices.

Identification strategy and setting

The policy setting involves a publicly financed expansion designed to reduce out-of-pocket expenditures and catastrophic health spending. Eligibility hinged on administrative criteria such as socioeconomic deprivation markers and/or household characteristics, and coverage was rolled out across states and districts in phases. These features create exogenous variation that can be harnessed to identify causal effects when combined with appropriate controls and fixed effects.

Three complementary strategies are emphasized:

• Difference-in-differences (DiD): Individuals or households eligible for coverage are compared to ineligible counterparts before and after program rollout. The key assumption is parallel trends in the absence of the program. Event-study plots diagnose pre-trend violations, while state-by-time and district-by-time fixed effects absorb policy and macroeconomic shocks.
• Instrumental variables (IV): Eligibility status or policy exposure instruments actual enrollment. The first stage captures the strength of eligibility in predicting coverage; F-statistics above conventional thresholds support instrument relevance. Exclusion restrictions are probed by testing for direct effects of eligibility on outcomes in pre-policy periods and through placebo outcomes not plausibly affected by coverage.
• Propensity score and reweighting: Within DiD or cross-sectional IV, balancing weights address observable differences in age, sex, education, baseline health, and urbanicity. Covariate balance is assessed by standardized differences and variance ratios, aiming for |SMD| < 0.1 post-weighting.

Outcomes reflect clinically meaningful cardiometabolic domains: systolic and diastolic blood pressure, capillary or venous glucose (fasting or random, with harmonized adjustment), body mass index, and indices of central adiposity such as waist circumference. Binary indicators defined by clinical thresholds (e.g., hypertension, diabetes, and overweight/obesity) complement continuous measures to capture both population shifts and risk category transitions. Medication use and diagnosis awareness, when available, are modeled as mediators to illuminate behavioral and treatment pathways between coverage and physiological risk factor levels.

Controls include demographics, household wealth or consumption proxies, education, caste category, and urban-rural residence. Health system features are proxied by facility density, availability of antihypertensives and antihyperglycemics, and historical utilization baselines. Fixed effects at the state or district level capture time-invariant regional differences, while survey wave or year fixed effects absorb national shocks. Standard errors are clustered at the policy assignment level (e.g., district) to account for serial correlation and shared exposure.

Effects, magnitudes, and heterogeneity

Across specifications, ITT estimates for physiologic outcomes tend to be modest in magnitude. Point estimates generally indicate limited short-term shifts in mean systolic blood pressure and glycemia, with 95% CIs frequently spanning the null. Treatment-on-the-treated effects obtained via IV are larger in absolute value than ITT, as expected, but remain small relative to clinical targets. For adiposity metrics, estimates center near zero with wide intervals, consistent with the long time horizon required for weight change following expanded financial access.

In contrast, process measures respond more readily. Coverage is associated with higher probability of ever being screened for blood pressure or glucose and increased diagnosis awareness, consistent with reduced financial barriers to first contact. Medication uptake moves in the anticipated direction. However, the translation from screening and initiation to sustained control appears attenuated, likely reflecting medication stock-outs, co-payments for outpatient drugs in some regions, and variability in follow-up adherence. This pattern aligns with an economic mechanism: insurance that primarily finances inpatient or episodic care improves contact and initiation but only partially addresses the recurring costs and logistics of chronic disease management.

Heterogeneity analyses are informative. Effects are more favorable among households in lower wealth quintiles, where the marginal utility of financial protection is highest, and in districts with better primary care density and public sector pharmaceutical availability. Urban areas exhibit somewhat larger gains in diagnosis, while rural areas show smaller but still directionally positive changes where facility access improves. Among individuals with baseline uncontrolled hypertension or dysglycemia, coverage associates with improved treatment uptake, yet the measured reductions in mean blood pressure and glucose remain modest over the observed horizon.

Subgroup exploration by age and sex suggests relatively stronger process changes in middle-aged adults, the group most active in the labor market and at rising cardiometabolic risk. Sex differences are modest, with women showing slightly larger gains in screening where maternal and child health platforms provide points of contact. In sensitivity checks that exclude individuals with prior cardiovascular events or pregnancy, patterns are qualitatively preserved, implying that acute care episodes do not solely drive observed associations.

Taken together, the magnitudes are consistent with a policy that predominantly reduces catastrophic expenditure risk and improves access to diagnostics, while realizing only incremental gains in physiologic control absent aligned medication financing and continuity of care. From a clinical perspective, the effect sizes are smaller than those typically sought in trials of antihypertensive or antihyperglycemic regimens; nevertheless, even small population shifts can be meaningful when scaled nationally, particularly if accumulative over time. Estimates reported with 95% CIs and p-values emphasize statistical uncertainty and the importance of interpretation through the lens of implementation context.

Robustness, validity, and interpretation

Internal validity rests on the plausibility of the identifying assumptions. Event-study analyses show flat pre-trends in outcomes between eligible and ineligible groups prior to rollout, supporting the DiD framework. Where minor pre-trend deviations exist, models that flexibly control for group-specific trends reduce bias while maintaining precision. IV first-stage statistics exceed conventional thresholds, and falsification outcomes not plausibly affected by coverage (e.g., height in adults) show no effect, lending credibility to the exclusion restriction.

Alternative outcome specifications confirm the main message. Winsorizing extreme biomarker values, applying inverse-probability weights to address nonresponse in biomarker modules, and using median regressions all yield similar directional conclusions. Modeling binary outcomes with linear probability models versus logistic regression does not meaningfully alter the inference. Results are robust to excluding states that implemented parallel health reforms or to reweighting by state population size to mitigate the influence of large states.

Measurement issues are transparently addressed. Blood pressure readings follow standardized protocols with multiple measurements; models rely on the final or mean of latter readings to reduce white-coat effects. For glucose, harmonization distinguishes fasting versus random measurements where data permit, and sensitivity analyses use z-score standardization within measurement type to ensure comparability. Anthropometric measures are cross-checked with heaping diagnostics; outlier handling minimally changes estimates.

Spillovers are considered. If insurance coverage increases facility utilization broadly, ineligible individuals in high-coverage districts might experience congestion or, conversely, benefit from system improvements. Difference-in-difference-in-differences (DDD) models appraise such spillovers by introducing district-by-time coverage intensity and testing for differential shifts among ineligible individuals; no consistent adverse spillover is detected, and patterns remain compatible with modest improvements concentrated among eligibles.

External validity requires caution. The specific benefit package and cost-sharing rules matter. When coverage emphasizes inpatient care without comprehensive outpatient drug coverage or chronic disease management benefits, the pathway from insurance to biomarker improvement is indirect. The observed small effects on physiologic outcomes alongside clearer gains in screening and treatment initiation point to the need for aligned financing of essential medicines, refill continuity, and team-based primary care. Where programs integrate outpatient pharmaceuticals and chronic care follow-up, larger effects on blood pressure and glycemia would be anticipated over longer horizons.

Mechanistically, several channels can connect coverage to risk factor levels:

• Price effect: Reducing effective prices for diagnostics and visits increases demand for screening and initial treatment.
• Income effect: Lower out-of-pocket risk frees household budget for medications and healthy goods, potentially improving adherence and diet quality.
• Supply response: Facility empanelment and reimbursement can reorient provider effort toward covered services, increasing availability of measurement and counseling.
• Behavioral response: Insurance status may increase perceived access, shifting health-seeking behavior and follow-up adherence.

The empirical patterns suggest the price and behavioral channels operate for screening and initiation, while income and supply responses are insufficient to fully overcome barriers to chronic medication continuity and lifestyle change. This interpretation aligns with widespread evidence that long-term cardiometabolic control hinges on consistent access to affordable medicines, frequent monitoring, and counseling, which are only partly mediated by insurance schemes that focus on inpatient benefits.

From a methods standpoint, the analysis illustrates good practice in quasi-experimental evaluation of health policy:

• Explicit definition of estimands (ITT and LATE) and clear first-stage reporting, including F-statistics and compliance rates.
• Graphical pre-trend diagnostics and event-study coefficients with confidence bands to test parallel trends.
• Sensitivity to clustering level and small-sample corrections to maintain proper inference under few clusters.
• Balance diagnostics for observable covariates, reported as standardized mean differences and variance ratios.
• Placebo tests in pre-policy periods and with non-affected outcomes, discouraging over-interpretation.

The overarching message is measured: insurance expansion shows promise as a platform for improved detection and initial management of cardiometabolic conditions, yet measurable changes in blood pressure, glycemia, and adiposity over the short to medium term are limited. Policy designs that bundle primary care access, essential drug coverage with minimal co-payments, and longitudinal care pathways are more likely to yield clinically meaningful reductions in risk factor levels. For clinicians, this underscores the importance of linking patients to affordable medication refills and follow-up, even when insurance status improves.

Future work could examine longer follow-up windows to capture cumulative effects, leverage additional quasi-experimental variation from benefit design changes (e.g., addition of outpatient drug coverage), and integrate provider-side data to quantify supply responses. Incorporating high-frequency measures of adherence and refill continuity would clarify mechanisms. Finally, evaluating heterogeneity by baseline control status can identify groups most likely to benefit from targeted interventions layered atop financial protection.