Health-related quality of life and its determinants among cancer patients: evidence from 12,148 patients of Indian database.

BACKGROUND: Cancer survivors experience a decrement in health-related quality of life (HRQoL) resulting from the disease as well as adverse effects of therapy. We evaluated the HRQoL of cancer patients, stratified by primary cancer site, stage, treatment response and associated adverse events, along with its determinants. METHODS: Data were collected from 12,148 patients, sampled from seven purposively chosen leading cancer hospitals in India, to elicit HRQoL using the EuroQol questionnaire comprising of 5-dimensions and 5-levels (EQ-5D-5L). Multiple linear regression was used to determine the association between HRQoL and various socio-demographic as well as clinical characteristics. RESULTS: Majority outpatients (78.4%) and inpatients (81.2%) had solid cancers. The disease was found to be more prevalent among outpatients (37.5%) and inpatients (40.5%) aged 45-60 years and females (49.3-58.3%). Most patients were found to be in stage III (40-40.6%) or stage IV (29.4-37.3%) at the time of recruitment. The mean EQ-5D-5 L utility score was significantly higher among outpatients [0.630 (95% CI: 0.623, 0.637)] as compared to inpatients [0.553 (95% CI: 0.539, 0.567)]. The HRQoL decreased with advancing cancer stage among both inpatients and outpatients, respectively [stage IV: (0.516 & 0.557); stage III (0.609 & 0.689); stage II (0.677 & 0.713); stage I (0.638 & 0.748), p value < 0.001]. The outpatients on hormone therapy (B = 0.076) showed significantly better HRQoL in comparison to patients on chemotherapy. However, palliative care (B=-0.137) and surgery (B=-0.110) were found to be associated with significantly with poorer HRQoL paralleled to chemotherapy. The utility scores among outpatients ranged from 0.305 (bone cancer) to 0.782 (Leukemia). Among hospitalized cases, the utility score was lowest for multiple myeloma (0.255) and highest for testicular cancer (0.771). CONCLUSION: Older age, lower educational status, chemotherapy, palliative care and surgery, advanced cancer stage and progressive disease were associated with poor HRQoL. Our study findings will be useful in optimising patient care, formulating individualized treatment plan, improving compliance and follow-up.

Does Public Spending Reflect the Need for Health: A Cross-sectional Analysis at District Level in India.

BACKGROUND: There is mixed evidence on the extent of association between the allocation of public revenue for healthcare and its indicators of need. OBJECTIVE: In this study, we examined the relationship between allocations through state health financing (SHF) and the Central Government with infant mortality. MATERIALS AND METHODS: District-wise infant mortality rate (IMR) was computed using National Family Health Survey-4 data. State-wise data for health budgets through SHF and National Health Mission (NHM, a Centrally Sponsored Scheme), were obtained for the year 2015-16. We used a multivariable analysis through generalized linear model method using identity-link function. RESULTS: We found per capita SHF (₹3169) to be more than 12 times that of public health spending per capita through NHM (₹261). IMR was lower in districts with higher SHF allocation, although statistically insignificant. The allocation through NHM was higher in districts with higher IMR, which is statistically significant. Every unit percentage increase in per capita net state domestic product and female literacy led to 0.31% and 0.54% decline, while a 1% increase in under-five diarrhoea prevalence led to 0.17% increase in IMR. CONCLUSION: The NHM has contributed to enhancing vertical equity in health-care financing. The States' need to be more responsive to the differences in districts while allocating health-care resources. There needs to be a focus on spending on social determinants, which should be the cornerstone for any universal health coverage strategy.

Strengthening Comprehensive Primary Health Care through Ayushman Bharat: Role of Demand and Supply-side Interventions.

BACKGROUND: Comprehensive primary health care (CPHC) is an effective way to respond to the challenges of changing epidemiology, growing population expectations, and universal health coverage. A set of demand and supply improvement strategies was developed to support primary health center provision and pilot tested in three model health and wellness centers (HWCs) in Punjab. OBJECTIVE: The study aimed to assess the early effects of interventions on the inputs, processes, and outputs for optimal implementation of the AyushmanBharat-HWC (AB-HWC) program. MATERIALS AND METHODS: Cross-sectional facility assessments were conducted using a standardized methodology at three time points to identify the changes in inputs and processes at subcenter-HWCs from 2019 to 2021. In addition, daily and month-wise service utilization data of model HWCs and nonmodel HWCs in the intervention block and control block in a district of Punjab from the AB-HWC portal were analyzed from May 2020 to April 2021. RESULTS: The difference-in-difference analysis indicated that the CPHC strengthening interventions in the model HWCs improved the mean number of people screened for noncommunicable diseases, mean newly diagnosed patients with hypertension and diabetes, mean hypertensive and diabetic patients on treatment, mean outpatient attendance, and mean number of wellness sessions by 265.71, 21.31, 29.48, 102.17, and 4.88 units per month, compared to control HWCs. CONCLUSION: The success of the initiatives can be attributed to an integrated approach encompassing multistakeholder planning of interventions, community involvement, empowerment of service providers, and consistent supportive supervision. The long-term success will be contingent on the quality of training, team dynamics, community participation, social accountability, and supervision support.

Development of an EQ-5D Value Set for India Using an Extended Design (DEVINE) Study: The Indian 5-Level Version EQ-5D Value Set.

OBJECTIVES: This study aimed to develop the Indian 5-level version EQ-5D (EQ-5D-5L) value set, which is a key input in health technology assessment for resource allocation in healthcare. METHODS: A cross-sectional survey using the EuroQol Group's Valuation Technology was undertaken in a representative sample of 3548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. The participants were interviewed using a computer-assisted personal interviewing technique. This study adopted a novel extended EuroQol Group's Valuation Technology design that included 18 blocks of 10 composite time trade-off (c-TTO) tasks, comprising 150 unique health states, and 36 blocks of 7 discrete choice experiment (DCE) tasks, comprising 252 DCE pairs. Different models were explored for their predictive performance. Hybrid modeling approach using both c-TTO and DCE data was used to estimate the value set. RESULTS: A total of 2409 interviews were included in the analysis. The hybrid heteroscedastic model with censoring at -1 combining c-TTO and DCE data yielded the most consistent results and was used for the generation of the value set. The predicted values for all 3125 health states ranged from -0.923 to 1. The preference values were most affected by the pain/discomfort dimension. CONCLUSIONS: This is the largest EQ-5D-5L valuation study conducted so far in the world. The Indian EQ-5D-5L value set will promote the effective conduct of health technology assessment studies in India, thereby generating credible evidence for efficient resource use in healthcare.

Health-related quality of life among breast cancer patients in India.

PURPOSE: This study evaluated the health-related quality of life (HRQoL) among breast cancer patients during various phases of treatment and with different treatment modalities, which helps in monitoring treatment outcomes, assessing the well-being of patients, and conducting health technology assessments. METHODS: A total of 534 interviews were conducted among the patients of breast cancer recruited at different stages of disease and with different treatment modalities. HRQoL was determined using EuroQoL five dimensions questionnaire with five levels (EQ-5D-5L), EuroQoL Visual Analogue Scale (EQ VAS), and the EORTC QLQ-BR23 instrument. The utility values were determined based on the Indian EQ-5D-5L value set. The socio-demographic and clinico-therapeutic determinants of HRQoL were evaluated using multiple linear regression. RESULTS: The mean utility value of breast cancer patients was 0.602 (SD = 0.311) and mean EQ VAS score was 75 (SD = 12.3). The mean utility value at diagnosis was 0.628, whereas utility value was 0.55, 0.595, and 0.64 for post-surgery, post-chemotherapy, and post-radiotherapy treatment groups, respectively. The most frequently reported problem was pain/discomfort (in 84.3% patients), followed by anxiety/depression (83.5%). On EORTC QLQ-BR23, the maximum symptom scale scores for systemic therapy side effects were reported in the post chemotherapy group. The body image score and future perspective score were better in patients undergoing breast conservative surgery (BCS) compared to patients undergoing modified radical mastectomy. Age, education, and employment status of the patient, type of treatment modality, and use of taxanes are the determinants of HRQoL in breast cancer patients. CONCLUSION: Clinical interventions should focus upon management of pain and anxiety. BCS should be offered to all eligible patients as it is associated with better HRQoL. Addressing the factors that independently affect the HRQoL will help in improving the treatment compliance and outcomes.

Do people with different sociodemographic backgrounds value their health differently? Evaluating the role of positional objectivity.

Objective: The fundamental disconnect between the actual and the perceived health of an individual raises considerable skepticism on the self-reported health data as it may be confounded by an individual's socio-economic status. In this light, the present study aims to assess if people with different sociodemographic backgrounds value their health differently. Methods: The health-state valuation using time-trade off was performed in a cross-sectional survey among a representative sample of 2,311 adults from India. Individuals were selected using a multistage stratified random sampling from five Indian states to elicit their present health-state, and to perform the health-state valuation exercise using computer assisted personal interviewing. A single block of standardized health-states was valued by multiple individuals, each belonging to different socio-demographic group. The difference in the valuation of health was assessed using bivariate analysis. The impact of different sociodemographic factors on the health-state valuation was evaluated using Tobit regression model. Results: Differences in the valuation of health were observed among different groups of age, religion, family type, state of residence, substance abuse, presence of ailments at the time of valuation, and number of dependent members in the household. Even after controlling for the severity of the administered health states, factors having a significant association with the valuation of health are age, religion, state of residence, substance abuse, family type, number of dependent members in the household, and presence of chronic or both acute and chronic ailments. Younger individuals place a higher value to their health as compared to their older counterparts. As compared to a healthy individual, a person with ailments rates the same health-state as worse. Conclusion: Inequalities in self-reported ill-health cannot be attributed to positional objectivity; age, religion, state of residence, substance abuse, family type, dependents, and ailments impact individual health valuation.

Health-related quality of life among Indian population: The EQ-5D population norms for India.

Background: The EuroQol 5 dimensions (EQ-5D) is the most used generic health-related quality of life (HRQoL) instrument for measuring population health and health outcomes. Since there are no EuroQol 5 dimensions 5 levels (EQ-5D-5L) population norms available for India, this study developed the Indian population norms for the EQ-5D-5L. The potential influencing factors of HRQoL of the Indian population have been identified. Methods: The data was collected alongside the Indian EQ-5D-5L valuation study (Development of an EQ-5D Value Set for India Using an Extended Design: DEVINE Study). A cross-sectional survey of 3548 adult respondents was conducted across five states of India, in which respondents were asked to report their own health states using the EQ-5D-5L descriptive system and the EuroQol Visual Analog Scale (EQ VAS). The utility score was calculated using the EQ-5D-5L value set based on the preferences of the Indian population. Norm scores were generated for age, sex, and other important socio-demographic variables. The proportion of patients reporting problems in different dimensions of EQ-5D-5L was assessed. The impact of socio-economic determinants on health-related quality of life was evaluated using multiple linear regression. Results: The mean EQ VAS score of the Indian population is 75.18 (95% confidence interval (CI) = 74.50-75.90), whereas mean utility score is 0.848 (95% CI = 0.840-0.857). The EQ VAS scores, and utility scores decreased with age. Males reported higher EQ VAS values than females. The highest mean utility score was observed for males of <20 years (0.936), whereas the lowest mean score was observed for females of >70 years (0.488). The mean VAS score ranged between 85.24 for females of <20 years and 50.67 for females of >70 years. Highest problems were reported in the dimension of "pain / discomfort", closely followed by "anxiety / depression". Age, educational qualification, marital status, substance abuse, presence of ailments, state / region of residence, number of dependent members in the household, and time spent on mobile are the significant determinants of HRQoL of Indian population. Conclusions: These population norms will be used as reference values for comparative purposes in future Indian studies. Economic evaluations can use these average age-specific HRQoL population norms to value the health-state of not having the specific disease under investigation.

Impact of India's publicly financed health insurance scheme on public sector district hospitals: a health financing perspective.

Background: Districts hospitals in India play a pivotal role in delivering health care services in the public sector and are empanelled under India's national health insurance scheme i.e. Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (PMJAY). In this paper, we evaluate the extent to which the PMJAY impacts the district hospitals from a financing perspective. Methods: We used cost data from India's nationally representative costing study-'Costing of Health Services in India' (CHSI) to determine the incremental cost of treating PMJAY patients, after adjusting for resources that are paid through supply-side government financing route. Second, we used data on number and claim value paid to public district and sub-district hospitals during 2019, to determine the additional revenue generated through PMJAY. The annual net financial gain per district hospital was estimated as the difference between payments under PMJAY, and the incremental cost of delivering the services. Findings: At current levels of utilisation, the district hospitals in India gain a net annual financial benefit of $ 26.1 (₹ 1839.3) million, which can potentially increase up to $ 41.8 (₹ 2942.9) million with an increase in the share of patient volume. For an average district hospital, we estimate net annual financial gain of $ 169,607 (₹ 11.9 million), increasing up to $ 271,372 (₹ 19.1 million) per hospital with increased utilisation. Interpretation: Demand-side financing mechanisms can be used to strengthen the public sector. Increasing utilisation of district hospitals, by either gatekeeping or improving availability of services will enhance financial gains for district hospitals and strengthen public sector. Funding: Department of Health Research, Ministry of Health & Family Welfare, Government of India.

Refining the provider payment system of India's government-funded health insurance programme: an econometric analysis.

OBJECTIVES: Reimbursement rates in national health insurance schemes are frequently weighted to account for differences in the costs of service provision. To determine weights for a differential case-based payment system under India's publicly financed national health insurance scheme, the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PM-JAY), by exploring and quantifying the influence of supply-side factors on the costs of inpatient admissions and surgical procedures. DESIGN: Exploratory analysis using regression-based cost function on data from a multisite health facility costing study-the Cost of Health Services in India (CHSI) Study. SETTING: The CHSI Study sample included 11 public sector tertiary care hospitals, 27 public sector district hospitals providing secondary care and 16 private hospitals, from 11 Indian states. PARTICIPANTS: 521 sites from 57 healthcare facilities in 11 states of India. INTERVENTIONS: Medical and surgical packages of PM-JAY. PRIMARY AND SECONDARY OUTCOME MEASURES: The cost per bed-day and cost per surgical procedure were regressed against a range of factors to be considered as weights including hospital location, presence of a teaching function and ownership. In addition, capacity utilisation, number of beds, specialist mix, state gross domestic product, State Health Index ranking and volume of patients across the sample were included as variables in the models. Given the skewed data, cost variables were log-transformed for some models. RESULTS: The estimated mean costs per inpatient bed-day and per procedure were 2307 and 10 686 Indian rupees, respectively. Teaching status, annual hospitalisation, bed size, location of hospital and average length of hospitalisation significantly determine the inpatient bed-day cost, while location of hospital and teaching status determine the procedure costs. Cost per bed-day of teaching hospitals was 38-143.4% higher than in non-teaching hospitals. Similarly, cost per bed-day was 1.3-89.7% higher in tier 1 cities, and 19.5-77.3% higher in tier 2 cities relative to tier 3 cities, respectively. Finally, cost per surgical procedure was higher by 10.6-144.6% in teaching hospitals than non-teaching hospitals; 12.9-171.7% higher in tier 1 cities; and 33.4-140.9% higher in tier 2 cities compared with tier 3 cities, respectively. CONCLUSION: Our study findings support and validate the recently introduced differential provider payment system under the PM-JAY. While our results are indicative of heterogeneity in hospital costs, other considerations of how these weights will affect coverage, quality, cost containment, as well as create incentives and disincentives for provider and consumer behaviour, and integrate with existing price mark-ups for other factors, should be considered to determine the future revisions in the differential pricing scheme.

Evaluation of EuroQol Valuation Technology (EQ-VT) Designs to Generate National Value Sets: Learnings from the Development of an EQ-5D Value Set for India Using an Extended Design (DEVINE) Study.

INTRODUCTION: Countries develop their EQ-5D-5L value sets using the EuroQol Valuation Technology (EQ-VT) protocol. This study aims to assess if extension in the conventional EQ-VT design can lead to development of value sets with improved precision. METHODS: A cross-sectional survey was undertaken in a representative sample of 3,548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. A novel extended EQ-VT design was created that included 18 blocks of 10 health states, comprising 150 unique health states and 135 observations per health state. In addition to the standard EQ-VT design, which is based on 86 health states and 100 observations per health state, 3 extended designs were assessed for their predictive performance. The extended designs were created by 1) increasing the number of observations per health state in the design, 2) increasing the number of health states in the design, and 3) implementing both 1) and 2) at the same time. Subsamples of the data set were created for separate designs. The root mean squared error (RMSE) and mean absolute error (MAE) were used to measure the predictive accuracy of the conventional and extended designs. RESULTS: The average RMSE and MAE for the standard EQ-VT design were 0.055 and 0.041, respectively, for the 150 health states. All 3 types of design extensions showed lower RMSE and MAE values as compared with the standard design and hence yielded better predictive performance. RMSE and MAE were lowest (0.051 and 0.039, respectively) for the designs that use a greater number of health states. Extending the design with inclusion of more health states was shown to improve the predictive performance even when the sample size was fixed at 1,000. CONCLUSION: Although the standard EQ-VT design performs well, its prediction accuracy can be further improved by extending its design. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state. HIGHLIGHTS: The EQ-5D-5L value sets are developed using the standardized EuroQol Valuation Technology (EQ-VT) protocol. This is the first study to empirically assess how much can be gained from extending the standard EQ-VT design in terms of sample size and/or health states. It not only presents useful insights into the performance of the standard design of the EQ-VT but also tests the potential extensions in the standard EQ-VT design in terms of increasing the health states to be directly valued as well as the number of observations recorded to predict the utility value of each of these health states.The study demonstrates that the standard EQ-VT design performs good, and an extension in the design of the standard EQ-VT can lead to further improvement in its performance. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state. Extending the design with inclusion of more health states marginally improves the predictive performance even when the sample size was fixed at 1,000.The findings of the study will streamline the systematic process for generating precise EQ-5D-5L value sets, thus facilitating the conduct of credible, transparent, and robust outcome valuation in health technology assessments.

Financial toxicity of cancer treatment in India: towards closing the cancer care gap.

Background: The rising economic burden of cancer on patients is an important determinant of access to treatment initiation and adherence in India. Several publicly financed health insurance (PFHI) schemes have been launched in India, with treatment for cancer as an explicit inclusion in the health benefit packages (HBPs). Although, financial toxicity is widely acknowledged to be a potential consequence of costly cancer treatment, little is known about its prevalence and determinants among the Indian population. There is a need to determine the optimal strategy for clinicians and cancer care centers to address the issue of high costs of care in order to minimize the financial toxicity, promote access to high value care and reduce health disparities. Methods: A total of 12,148 cancer patients were recruited at seven purposively selected cancer centres in India, to assess the out-of-pocket expenditure (OOPE) and financial toxicity among cancer patients. Mean OOPE incurred for outpatient treatment and hospitalization, was estimated by cancer site, stage, type of treatment and socio-demographic characteristics. Economic impact of cancer care on household financial risk protection was assessed using standard indicators of catastrophic health expenditures (CHE) and impoverishment, along with the determinants using logistic regression. Results: Mean direct OOPE per outpatient consultation and per episode of hospitalization was estimated as ₹8,053 (US$ 101) and ₹39,085 (US$ 492) respectively. Per patient annual direct OOPE incurred on cancer treatment was estimated as ₹331,177 (US$ 4,171). Diagnostics (36.4%) and medicines (45%) are major contributors of OOPE for outpatient treatment and hospitalization, respectively. The overall prevalence of CHE and impoverishment was higher among patients seeking outpatient treatment (80.4% and 67%, respectively) than hospitalization (29.8% and 17.2%, respectively). The odds of incurring CHE was 7.4 times higher among poorer patients [Adjusted Odds Ratio (AOR): 7.414] than richest. Enrolment in PM-JAY (CHE AOR = 0.426, and impoverishment AOR = 0.395) or a state sponsored scheme (CHE AOR = 0.304 and impoverishment AOR = 0.371) resulted in a significant reduction in CHE and impoverishment for an episode of hospitalization. The prevalence of CHE and impoverishment was significantly higher with hospitalization in private hospitals and longer duration of hospital stay (p < 0.001). The extent of CHE and impoverishment due to direct costs incurred on outpatient treatment increased from 83% to 99.7% and, 63.9% to 97.1% after considering both direct and indirect costs borne by the patient and caregivers, respectively. In case of hospitalization, the extent of CHE increased from 23.6% (direct cost) to 59.4% (direct+ indirect costs) and impoverishment increased from 14.1% (direct cost) to 27% due to both direct and indirect cost of cancer treatment. Conclusion: There is high economic burden on patients and their families due to cancer treatment. The increase in population and cancer services coverage of PFHI schemes, creating prepayment mechanisms like E-RUPI for outpatient diagnostic and staging services, and strengthening public hospitals can potentially reduce the financial burden among cancer patients in India. The disaggregated OOPE estimates could be useful input for future health technology analyses to determine cost-effective treatment strategies.

Fun30 and Rtt109 Mediate Epigenetic Regulation of the DNA Damage Response Pathway in C. albicans.

Fun30, an ATP-dependent chromatin remodeler from S. cerevisiae, is known to mediate both regulation of gene expression as well as DNA damage response/repair. The Fun30 from C. albicans has not yet been elucidated. We show that C. albicans Fun30 is functionally homologous to both S. cerevisiae Fun30 and human SMARCAD1. Further, C. albicans Fun30 can mediate double-strand break end resection as well as regulate gene expression. This protein regulates transcription of RTT109, TEL1, MEC1, and SNF2-genes that encode for proteins involved in DNA damage response and repair pathways. The regulation mediated by C. albicans Fun30 is dependent on its ATPase activity. The expression of FUN30, in turn, is regulated by histone H3K56 acetylation catalyzed by Rtt109 and encoded by RTT109. The RTT109Hz/FUN30Hz mutant strain shows sensitivity to oxidative stress and resistance to MMS as compared to the wild-type strain. Quantitative PCR showed that the sensitivity to oxidative stress results from downregulation of MEC1, RAD9, MRC1, and RAD5 expression; ChIP experiments showed that Fun30 but not H3K56ac regulates the expression of these genes in response to oxidative stress. In contrast, upon treatment with MMS, the expression of RAD9 is upregulated, which is modulated by both Fun30 and H3K56 acetylation. Thus, Fun30 and H3K56 acetylation mediate the response to genotoxic agents in C. albicans by regulating the expression of DNA damage response and repair pathway genes.

Patch-Based Dual-Tree Complex Wavelet Transform for Kinship Recognition.

Kinship recognition is a prominent research aiming to find if kinship relation exists between two different individuals. In general, child closely resembles his/her parents more than others based on facial similarities. These similarities are due to genetically inherited facial features that a child shares with his/her parents. Most existing researches in kinship recognition focus on full facial images to find these kinship similarities. This paper first presents kinship recognition for similar full facial images using proposed Global-based dual-tree complex wavelet transform (G-DTCWT). We then present novel patch-based kinship recognition methods based on dual-tree complex wavelet transform (DT-CWT): Local Patch-based DT-CWT (LP-DTCWT) and Selective Patch-Based DT-CWT (SP-DTCWT). LP-DTCWT extracts coefficients for smaller facial patches for kinship recognition. SP-DTCWT is an extension to LP-DTCWT and extracts coefficients only for representative patches with similarity scores above a normalized cumulative threshold. This threshold is computed by a novel patch selection process. These representative patches contribute more similarities in parent/child image pairs and improve kinship accuracy. Proposed methods are extensively evaluated on different publicly available kinship datasets to validate kinship accuracy. Experimental results showcase efficacy of proposed methods on all kinship datasets. SP-DTCWT achieves competitive accuracy to state-of-the-art methods. Mean kinship accuracy of SP-DTCWT is 95.85% on baseline KinFaceW-I and 95.30% on KinFaceW-II datasets. Further, SP-DTCWT achieves the state-of-the-art accuracy of 80.49% on the largest kinship dataset, Families In the Wild (FIW).

Combatting the imbalance of sex ratio at birth: medium-term impact of India's National Programme of Beti Bachao Beti Padhao in the Haryana State of India.

The Government of India initiated the Beti Bachao Beti Padhao (B3P) programme in 2015 as a flagship initiative to reduce gender imbalance in sex ratio at birth (SRB) and to ensure social protection of girls. The present study was conducted to evaluate the medium-term impact of B3P implementation in Haryana state, from 2015 to 2019, on SRB. Monthly data on SRB were collected for the entire state of Haryana through a civil registration system. Segmented time series regression analysis was used to estimate the variations in SRB after the B3P programme with the help of Winter's additive interrupted time series model. The SRB in Haryana increased from 876 girls per 1000 boys in 2015 to 923 in 2019. The results of the model demonstrated that before the inception of intervention (pre-slope), there was a significant monthly change in SRB of 0.217 (95% confidence interval: 0.144-0.290). Following the B3P programme, SRB was found to increase by 0.835 per month, which implied that an increase of 0.618 (confidence interval: 0.338, 0.898) every month in SRB can be attributed to the B3P programme. This indicated that SRB for the state of Haryana increased at the rate of 7.42 units per year as a result of the B3P programme. B3P has led to a significant improvement in SRB in Haryana state. The continuity of efforts in the same direction with a sustained focus on behaviour change will further help achieve the goal of gender parity in births and child survival.