Counting the Savings: Impact of Georgia's Drug Policy Interventions on Households  

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However, in Georgia, as in other low and middle-income countries, out-of-pocket payments for medicines remain a significant financial burden, particularly for low-income households. Despite implementing the Universal Health Coverage Program in 2013, medicines account for the largest share of OOP health expenditures, exacerbating the risk of impoverishment. This study uses interrupted time series analysis to evaluate the impact of four major pharmaceutical policy interventions initiated between 2017 and 2023 on household monthly drug expenditures. Methodology The analysis utilized pooled data from the 2015–2023 Household Income and Expenditure Surveys, covering over 110,000 households. Monthly median drug expenditures were adjusted to constant prices in January 2015 and analyzed. Three policy interventions were assessed: the 2017 drug reimbursement plan, the introduction of parallel imports from Turkey in 2022, and the implementation of external reference pricing in 2023. The regression models accounted for seasonality and complex survey design features, including weights and clustering. Results The results demonstrated that only the external reference pricing policy led to an immediate reduction of 6.96 GEL (p = 0.016) and a sustained monthly decline of 1.28 GEL/month (p = 0.002), representing a 29% reduction in monthly spending, which saved households an estimated 43.3 (95 % CI: 18.0: 68.9) million GEL in 2023. The 2022 parallel import policy showed an immediate decrease of 2.26 GEL (p = 0.39) but was followed by a significant upward trend (coefficient = 1.43, p < 0.001). Budget modifications earlier intervention in 2019 did not yield significant changes in spending levels or trends. Conclusion These findings emphasize the impact of external reference pricing in reducing the financial burden of pharmaceutical expenditures and underscore its potential as a viable policy option for other low—and middle-income countries. Nevertheless, based on the experience of different countries, sustained reductions in household spending on drugs necessitate ongoing monitoring and supplementary measures to address disparities in access and ensure a lasting, positive impact on the population. Experiences in Georgia could be educational for policymakers in low-middle-income settings. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Access to essential medicines is crucial in achieving the Sustainable Development Goals (SDGs) - "ensuring access to safe, effective, quality, and affordable essential medicines and vaccines for all" (1). They are also fundamental to achieving Universal Health Coverage (UHC) objectives, which guarantees everyone access to health services, including essential medicines, without the risk of financial hardship (2). Yet according to WHO estimates, up to 90% of people in low- and middle-income countries (LMICs) purchase medicines through out-of-pocket payments (OOPs). For some households, this expense may require selling an asset, such as a family cow, or pulling children out of school, ultimately pushing the family deeper into intergenerational poverty (3). Georgia presents a particularly compelling case on this matter, as the introduction of the Universal Health Coverage Program (UHCP) in 2013 expanded coverage to nearly 90% of the population to promote equitable access to healthcare for everyone. Despite this, among the poorest households facing catastrophic spending, outpatient medicines accounted for 90% of OOP payments for health services in 2018, compared to just 24% among the wealthiest households. Additionally, over the years, medicines have remained the most significant component of OOP payments, comprising 69% in 2018 (4). More recent studies have shown that over the years, households reporting OOP expenditure on drugs were 43 times more likely to experience impoverishing health expenditure (5). To address this issue, the Georgian government has implemented three major policies to reduce the burden of OOPs for medicines. These policy actions offer natural experiment for evaluating their impact and effectiveness. This study aims to assess the financial impact of three pharmaceutical policy actions in 2017-2023 on the population while offering valuable guidance for other LMICs striving to reduce out-of-pocket spending on pharmaceuticals through well-informed policy decisions. This study is part of a larger body of work that examines pharmaceutical policy and expenditures in Georgia (5) (Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia’s Chronic Disease Medicine Program) and provides additional context. This paper specifically focuses on assessing the impact of the stated policy actions using an interrupted time series (ITS) model to better understand which policy actions might have had the most impact on reducing OOPs for medicines. Background Georgia is a sovereign country located in the South Caucasus region of Eurasia. It is bordered by the Black Sea to the west, Russia to the north, Turkey and Armenia to the south, and Azerbaijan to the southeast ( 6 ). Until 1991, as part of the Soviet Union, Georgia's healthcare system operated under the Semashko model. After gaining independence and losing its 78% of GDP ( 7 ), the country began transitioning away from the Semashko model and implementing various changes in the healthcare system. During this time, the economy experienced a significant upturn, and as of 2023, Georgia was classified as an upper-middle-income country with GDP per capita measured at 8,210 current US $ ( 8 ). Nevertheless, the benefits of economic growth are not shared equally across society, as poverty remains a significant issue. In 2023, 19.8 percent of the population lived below the relative poverty line, while the Gini coefficient stood at 0.36 ( 9 ). One of the most significant changes in healthcare policy was carried out in 2013, when Georgia's newly elected government launched the UHCP, expanding public insurance coverage to over 90% of the population. Concurrently, current health expenditure (CHE) as a percentage of GDP saw a significant increase, reaching 7.26% in 2022, a figure relatively close to the WHO Europe region's 8.06% for the same year ( 10 ). However, a substantial portion of the CHE still comes from OOP payments, accounting for 40.45% in 2022, compared to the WHO European average of 26.79% for the same year ( 11 ). The introduction of the UHCP marked a significant milestone in expanding healthcare access, but its limitations have highlighted persistent challenges. While the basic package of services covered under UHCP includes emergency care, outpatient services, elective surgery (with necessary examinations and diagnostics), cancer treatment, childbirth, infectious disease management, and certain medications for chronic conditions, its primary focus has been on inpatient and emergency treatment ( 4 ). This prioritization has come at the expense of primary care and outpatient medicine coverage, leaving OOP spending on drugs as a substantial financial burden for households. Consequently, OOP drug expenses have become a major driver of healthcare costs, significantly increasing the likelihood of impoverishment, as shown in the recent analysis. Moreover, the limited pharmaceutical coverage and unregulated pricing have left patients vulnerable to escalating costs, further exacerbated by Georgia's reliance on imported medicines, whose prices rose by 41% between 2016 and 2020 ( 4 , 12 ). To reduce the burden of OOP expenditure on drugs, the government of Georgia initiated multiple policy actions over the course of the years. In April 2017, the drug reimbursement scheme for chronic conditions was initiated, which focused on providing subsidized medicines for the population with the four most prevalent chronic conditions: hypertension, chronic obstructive pulmonary disease (COPD), diabetes (type 2) and chronic thyroid gland diseases. This scheme required beneficiaries to co-pay part of medicine prices; the state-covered amounts varied according to the individual’s socioeconomic status ( 13 ). Over the years, both the number of beneficiaries and the program's budget have consistently grown (see Fig. 1 ). In 2018, the coverage was significantly extended to the retirement-age population and people with disabilities ( 14 ). In 2019, the list of chronic conditions increased, incorporating the subsidized provision of drugs for the population with Parkinson’s disease and epilepsy with a 25% co-payment, ( 4 ) leading to an increase in the number of beneficiaries and the allocated budget in subsequent years (see Fig. 1 ). In July 2020, the drug benefits program for patients with chronic diseases underwent significant changes. Instead of being a separate budget sub-program, it was integrated into the UHCP, and centralized procurement and distribution of drugs managed by MoH, was substituted with drug reimbursement scheme through private retail pharmacies, which helped reduce geographical access barriers as well as shortcomings of central procurement and distribution ( 15 ) (Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia’s Chronic Disease Medicine Program). Figure 1 Evolution of drug reimbursement plan budget and beneficiary population Another significant policy action took place in January 2022, with the initiation of parallel drug imports from Turkey to introduce more affordable drug alternatives into the country ( 16 ). Efforts to lower pharmaceutical prices continued in December 2022 with the implementation of External Reference Pricing (ERP) ( 17 ). The recent studies have indicated that the inflation index for medical products, appliances, and equipment declined in 2023 (see Fig. 2 ), most likely as a result of these measures ( 12 ) . Figure 2 Inflation Index for healthcare groups 2017-2023 Despite efforts to reduce OOP for pharmaceuticals, they continue to account for tht part of national drug expenditures, even with notable reductions in recent years (see Fig. 3), while for other services, the share of state funding is significantly higher. Therefore, evaluating household spending on pharmaceuticals in light of policy reforms remains essential to understanding and addressing the financial burden of out-of-pocket drug expenses in Georgia. Figure 3 Share of OOP spending on health care spending groups Methodology This analysis is based on pooled data from the 2015–2023 Household Income and Expenditure Surveys (HIES) conducted by Georgia's national statistical agency - GeoStat. These surveys employ a two-stage stratified cluster sampling method with census enumeration areas selected initially and followed by households within these areas. To account for regional and urban-rural differences, the sample is organized into 21 strata, with approximately 1,440 households randomly chosen each month. Every household is interviewed twice in two separate quarters within a year, and the same are revisited in the same quarters the following year to capture changes over time. After completing four rounds, about one-twelfth of the panel is replaced with the new households from the same cluster, helping maintain a consistent panel structure. The sampling frame is derived from the 2014 general population census, whereas before 2017, it was based on the 2002 census. Furthermore, until 2017, each household was surveyed consecutively across four quarters, leading to variations in data collection over time ( 18 ). GeoStat provides free access to online microdata and survey documentation from its website ( 19 ). The household-level dataset includes monthly consumption information categorized by the Classification of Individual Consumption by Purpose (COICOP) and a range of household characteristics. The individual member dataset captures demographic and socio-economic details, such as age, sex, and education of household members. For this study, the household-level data from the annual surveys conducted from 2015 to 2023 (excluding years 2017 and 2018) were combined into a single database (N: 88,332). Household-level characteristics were aggregated—either as attributes of the household head or as significant traits of at least one household member—for use in regression analysis. The dataset was refined to include only households with complete data on the relevant variables, yielding a final sample of 87,337 households (see Table 1 ). Three modification points were identified to assess policy changes and their impacts on financial well-being. First, instead of choosing 2017 as a time point for policy action for state-subsidized medicines for chronic diseases, we eliminated data for 2017 and 2018 due to low budget spending and low uptake of this program (see Fig. 1 ). Therefore, we selected 2019 as a time point for policy evaluation. For simplified pharmaceutical imports from Turkey we selected January 2022, and for the introduction of reference pricing December 2022 was chosen. The analysis was conducted in R (Version 2023.12.1 + 402) using an Interrupted Time Series (ITS) approach. The household-level database was aggregated monthly, with household median monthly pharmaceutical expenditure (identified by the COICOP classification) as the outcome variable. To control for inflation, all household health spending data (including for pharmaceuticals) was converted into constant 2015 January prices. Monthly aggregation helped enhance the model's predictive power. It allowed the examination of multiple intervention points related to pharmaceutical policy changes, whereas the median values adjusted for the highly positive skewed distribution (11.47) of monthly healthcare expenses on pharmaceuticals ( 20 , 21 ). Socio-economic and health profile variables were incorporated into the final model to account for changes in population over time (see Table 1 ). The selection of independent variables followed Andersen’s model of healthcare utilization ( 22 ) and was based on data availability within the dataset. Categorical variables were aggregated monthly as the share of households exhibiting particular characteristics, while mean values were used for continuous variables after assessing distribution normality. Because we used median drug expenditure values that do not explicitly account for households with no pharmaceutical expenditure, using the dichotomous variable, we derived the share of households with drug expenditure for each month and included it in the model. Additionally, another dichotomous variable indicating whether households incurred expenditures on other healthcare services, such as inpatient, outpatient, dental, or diagnostic services, was included in the dataset. This variable was aggregated monthly as a share of households reporting such expenditures. The complex survey design was considered when creating the time series using the "srvyr" package ( 23 ): household weights were used as sampling weights, stratum as the strata variable, and unique household identification numbers were used to account for the panel design. The final model was tested for seasonality by visually decomposing the outcome variable to assess periodic patterns over time. The visual inspection revealed a clear seasonal pattern addressed by incorporating Fourier terms into the model ( 21 ). Additionally, the autocorrelation of residuals was tested using the Durbin-Watson statistic, and no significant autocorrelation was detected, as indicated by the p-value exceeding the acceptable threshold ( 20 ). Results The descriptive analysis of the study sample revealed that most households did not have children under the age of 6 as members, while the majority included at least one elderly member. Furthermore, most households reported having at least one chronically ill member. Notably, around 70% of households reported expenditures on drugs over the years, making this the more frequently reported type of healthcare expenditure compared to all other healthcare services combined. As expected, households with at least one elderly member and at least one chronically ill member had higher monthly median expenditures on drugs. The spending was even higher for households consisting of at least one person with a disability as a member. Additionally, the vast majority of household heads reported having either public or private health insurance, indicating high population coverage with prepayment schemes, mostly attributed to UHCP. Table 1 Characteristics of the analytical sample Variable Unweighted count of HHs Weighted count of HHs % of all HHs Inflation adjusted Median monthly HH expenditure on drugs Total 87,337 7,428,169 100% 20.6 2015 10,770 1,022,671 13.8% 19.4 2016 10,606 1,037,894 14.0% 21.0 2019 13,578 1,042,563 14.0% 20.6 2020 12,996 1,061,698 14.3% 19.0 2021 13,291 1,090,179 14.7% 17.2 2022 13,211 1,070,849 14.4% 23.1 2023 12,885 1,102,314 14.8% 26.2 Male headed HHs 57,796 4,716,102 63.5% 19.3 Female headed HHs 29,541 2,712,067 36.5% 23.3 HH head without higher education 48,098 3,660,139 49.3% 19.5 HH head with higher education 39,239 3,768,030 50.7% 21.8 HHs without child members 72,271 6,062,276 81.6% 21.4 HHs with at least one child member 15,066 1,365,893 18.4% 17.4 HH without elder members 31,206 2,905,028 39.1% 3.4 HH with at lease one elder member 56,131 4,523,141 60.9% 32.9 Urban HHs 50,811 2,889,586 38.9% 21.0 Rural HHs 36,526 4,538,582 61.1% 20.4 HHs in top 3 per capita expenditure quintiles 49,388 4,460,321 60.0% 27.6 HHs in bottom 2 per capita expenditure quintiles 37,949 2,967,848 40.0% 13.9 HHs without a member with disability 78,873 6,776,944 91.2% 18.9 HHs with at least one member with disability 8,464 651,225 8.8% 43.5 HHs without a chronically ill member 29,556 2,670,946 36.0% 0.0 HHs with at least one chronically ill member 57,781 4,757,222 64.0% 36.6 HH head younger than 60 38,553 3,549,423 47.8% 6.8 HH head age older than 60 48,784 3,878,745 52.2% 33.8 HH with less than 3 members 50,499 4,166,275 56.1% 20.3 HH with more than 3 members 36,838 3,261,894 43.9% 21.2 HHs without additional healthcare expenditure on other services 63,834 5,316,786 71.6% 15.4 HHs with additional healthcare expenditure on other services 23,503 2,111,383 28.4% 37.7 HHs without expenditure on drugs 22,822 2,062,216 27.8% 0.0 HHs with expenditure on drugs 64,515 5,365,953 72.2% 35.8 HH head without a spouse 53,096 4,491,800 60.5% 19.9 HH head with a spouse 34,241 2,936,369 39.5% 21.4 HH head without insurance 286 17,157 0.2% 10.4 HH head with insurance 87,051 7,411,012 99.8% 20.6 The results of the regression analysis are detailed in Table 2 . Intercept − 40.01 (95% CI: − 443.61: 363.58; p = 0.85) indicates that the baseline household monthly spending level on medicines before any policy interventions was not significantly different from zero in our model. This suggests that baseline levels do not meaningfully explain household spending on medication over time. The coefficient for time (pre-intervention trend) was 0.08 (95% CI p = 0.4), which was not statistically significant, suggesting that inflation-adjusted household drug spending in constant January 2015 prices did not exhibit any statistically significant trend prior to policy interventions. The policy intervention of 2017 measured starting from 2019 had a coefficient of 1.94 (95% CI: -3.51:7.38; p = 0.488) and a trend estimate of -0.17 (95% CI: -0.41 : 0.07; p = 0.16), both statistically insignificant, indicating no notable impact on adjusted household median spending on drugs, possibly because still relatively low uptake of benefits by population and low financial limits paid by the government (see more about Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia’s Chronic Disease Medicine Program). For the second policy change in 2022, which simplified the parallel import of drugs from Turkey, the immediate effect was measured at -2.26 (95% CI: -7.39: 2.88; p = 0.392), while the overtime trend was measured at 1.43 (95% CI: 0.75: 2.12; p < 0.001). This suggests that the intervention did not initially reduce spending but was associated with a statistically significant post-intervention effect, leading to an increase in household drug spending over time. Finally, only the introduction of external reference pricing of 2023 (intervention 3), with a statistically significant immediate effect estimate of -6.96 (95% CI: -12.48: 1.45; p = 0.016) and trend estimates after the intervention of -1.28 (95% CI: -2.09: -0.48; p = 0.003) with relatively high statistical significance, showed a significant immediate reduction by 29% in household drug median spending, with a continuing significant decline over time (see Fig. 4 ). This suggests that external reference pricing was the most effective policy for reducing household spending on drugs, possibly due to successful price-control measures and adjustments in drug coverage. Insert Fig. 4 The influences of policy changes on the HHs median monthly expenditure on drugs over the study period In this model, most of the other variables, including household structure and income levels, were not statistically significant, indicating limited influence on adjusted drug spending. However, the mean age of household heads had a statistically significant coefficient of 1.65 (95% CI: 0.15: 3.14; p = 0.03), suggesting that higher drug spending was associated with older household heads. This is likely due to the increased healthcare needs due to widespread chronic conditions that require medications for treatment. Additionally, as expected, the control variable indicating the share of households encountering expenditure on drugs showed a significant effect of 0.42 (p < 0.014) on median monthly expenditure. Finally, statistically significant seasonal or cyclical variation in drug spending, measured with Fourier terms 1.14 (95% CI: 0.32: 1.97; p = 0.001), confirms that changing drug spending was due to seasonal changes in healthcare needs. Table 2 Interrupted time series results Estimate Lower_CI Upper_CI P_Value Intercept (40.01) (443.61) 363.58 0.8466 Time 0.08 (0.10) 0.25 0.4077 Intervention 1 1.94 (3.51) 7.38 0.4882 Time after intervention 1 (0.17) (0.41) 0.07 0.1608 Intervention 2 (2.26) (7.39) 2.88 0.3923 Time after intervention 2 1.43 0.75 2.12 0.0001 ** Intervention 3 (6.96) (12.48) (1.45) 0.0161 * Time after intervention 3 (1.28) (2.09) (0.48) 0.0027 ** Share of Female headed HH (0.19) (0.86) 0.48 0.5799 Share of HH heads without higher education 0.04 (0.33) 0.42 0.8289 Share of HH with children under 6 as members (0.26) (0.95) 0.42 0.4553 Share of HH with elder members (0.14) (0.66) 0.37 0.5798 Share of rural HHs 0.01 (0.29) 0.32 0.9343 Share of the poorest and 2nd poorest hhs 0.02 (0.15) 0.19 0.813 Share of HH s with members with disability 0.38 (0.30) 1.06 0.2754 Share of HH with chronically ill members (0.16) (0.61) 0.29 0.482 Mean Age of HH heads 1.65 0.15 3.14 0.0348 * Mean HH size (2.03) (12.41) 8.34 0.7022 Share of HH heads without a spouse 0.44 (0.21) 1.10 0.1901 Share of Insured HH heads (0.63) (4.50) 3.25 0.7528 Share of hhs encountering expenditure on healthcare services excluding drugs 0.17 (0.04) 0.38 0.1141 Share of hhs encountering expenditure on drugs 0.42 0.09 0.74 0.0138 * Fourier terms 1.14 0.32 1.97 0.0087 ** Significance level:. <0.1, *<0.05, **<0.001 The results indicate that the external reference pricing (ERP) - third policy intervention was effective at reducing household spending on pharmaceuticals, both immediately and in the longer term. Household characteristics, such as age and drug expenditure patterns, also influence spending levels, highlighting the role of demographic factors. The model’s high R 2 – 0.76 value further underscores the model’s effectiveness in explaining spending variations. These insights could be valuable for policymakers in assessing the long-term impact of policy interventions on pharmaceutical spending and refining future interventions to sustain reductions in household financial burden. Limitations Before discussing the results, we first would like to note the limitations of the study: To evaluate interventions using ITS, it is recommended to have at least 12 time points before and after the intervention ( 21 ). However, in the case of the second policy change, which focused on allowing parallel drug imports from Turkey, only 11 time points were available after its implementation and prior to the third assessed policy. This may impact the reliability of findings related to this policy action. The same argument can be made regarding evaluating the isolated impact of policy modifications related to chronic disease medicine program. The drug reimbursement program underwent an evaluation described elsewhere (Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia’s Chronic Disease Medicine Program). Government spending gradually rose from 2017 to 2023 (refer to Fig. 1 ); however, these annual changes cannot be integrated into the model due to their monthly variability and the absence of official documentation detailing when specific budget adjustments were made. Moreover, excluding the years 2017 and 2018 from the model may have introduced some bias in measuring the overall trend before policy implementation. While it is unlikely to have impacted the coefficients for policy change effectiveness, it remains a relevant consideration. Second, due to the absence of a specific utilization variable in the dataset, expenditure on health was used as a proxy to account for health service utilization. We do not view this as a significant limitation of the study, however, it is important to acknowledge. Finally, the impact of ERP may overlap with that of the Chronic drug reimbursement scheme, introduced in 2017, which took some years to pick up a pace and reach a higher number of beneficiaries in 2023. Therefore, the gradual evolution of this program and the benefits afforded to the population (Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia’s Chronic Disease Medicine Program) could have overlapping effects with the ERP, and the separation of two policy effects on monthly drug expenditure was not possible. Nonetheless, drug benefits did reach 276,977 beneficiaries (or 7.4 of the population %) before the end of 2021, but its effects were not visible in our analysis. .However, the steady increase in government spending on pharmaceuticals, particularly in recent years, suggests that the positive effects on drug expenditure may not be attributable to the ERP alone. Discussion The increased budges in drug reimbursement plan in 2019 did not significantly impact the population's out-of-pocket (OOP) spending on medications. Several factors likely contributed to its limited effectiveness: first, the program's budget, particularly in 2019 and 2020 was significantly lower than the overall OOP expenditure on medicines the population bears. Second, the number of beneficiaries in 2019 was insignificantly lower – 145,279 individuals compared to 473,048 (or 17% of the adult population) in 2023 ( 24 ). Finally, the plan's limited coverage, focusing only on a restricted list of chronic conditions and with financial limits for reimbursement, was also insufficient to offset OOP spending with government contributions. Significant progress was made with implementing the drug reimbursement program since 2020, when the program was moved to UHCP and budget allocations were increased and financial limits for reimbursement expanded. However, expanding the number of beneficiaries and extending the benefits took several years. The first major growth after 2019 was observed in 2021, with 276,977 beneficiaries, followed by substantial increases in 2022 and 2023, reaching 473,048 beneficiaries receiving drug benefits in 2023. Despite these advancements, the program's impact on the wellbeing of the population remained limited, as drug spending remained to be a major determinant of impoverishment even in 2023 ( 5 ). While the program did not provide financial protection to the households' it positively affected the balance between the government’s share of spending on drugs. Because drugs still remain a major impoverishing expense for households, a further increase in government spending on drugs seems necessary. Next, considering the limitations noted earlier, we did not observe any positive changes accompanying parallel imports from Turkey. However, other studies that looked at possible links between simplified imports from Turkey in 2022 and drug expenditure noted reductions in price growth (negative inflation) on medications (see Fig. 2 ) ( 12 ), nonetheless, this positive effect was not confirmed in our model. Nevertheless, this finding should be interpreted cautiously due to the model's limitations, particularly its failure to meet the minimum requirement of 12-time points, resulting in insufficient statistical power. ( 20 ). The external reference pricing introduced in 2023 had the most pronounced effect in reducing average household monthly spending on medicines. Marked by a one-time drop in pharmaceutical expenditure by 6.96 Gel (95% CI:-12.48:- 1.45) followed by a sustained monthly decline by 1.28 Gel (95% CI:-2.09:-0.48) in constant 2015 January prices. Without this policy, Georgia's population would have spent an additional 43.44 43.3 (95% CI: 18.0: 68.87) million GEL (in nominal 2023 GEL) on pharmaceuticals over 12 months in 2023. The fiscal impact of the reform can be attributed to two factors: first, the broad reach of the reference pricing policy, which affected all segments of society and every household that incurred expenses on medicines (74% of the households in 2023); and second, the expansion of state-subsidized medicine beneficiaries in 2023 which reached 17% of the adult population ( 24 ). Therefore, as noted in the limitations, the impact likely represents the combined effects of both policy actions, making it challenging to determine the isolated impact of each policy action alone. Nonetheless, existing literature emphasizes the strong potential of ERP reforms to reduce pharmaceutical costs ( 25 – 27 ). And comparable positive effect on controlling pharmaceutical costs has been documented in low- and middle-income countries (LMICs) elsewhere ( 28 ). Moreover, the findings of our study suggesting positive influence on household’s financial protection from drug expenditures being a result of a cumulative effect of two or more policy interventions studied that targeting different attributes of health financing system (revenues rising, pooling and purchasing) is consistent with the recent systematic literature review, which argues that while a combination of policy interventions targeting more than one attribute is needed for positive impact on financial protection ( 29 ). Conclusion Our study uncovered positive results that can be plausibly linked to ERP policy and drug reimbursement schemes introduced in Georgia. However, other countries' experiences indicate that ERP policy's benefits may evaporate over time ( 30 ). According to the review of international experience, as ERP policies become more widely used, it may drive manufacturers to adopt counterstrategies, such as imposing narrow list price corridors or delaying/avoiding product launches in a given geography, which could exacerbate geographic inequities in access to new products ( 31 ). Therefore, Georgia, learning from the experiences in other countries, has to closely monitor the price movements and their impact on the population and, when necessary, should consider revising and updating the ERP policy to achieve more favorable outcomes for its citizens ( 32 ). Furthermore, ERP reform should be combined with other regulatory and policy measures for optimal results ( 29 , 33 ). This appears to be the case in Georgia, where the study's results likely reflect the combined impact of changes in drug reimbursement plan and the ERP and the country may need to further enhance drug benefits program. Such enhancement could entail improving program effectiveness, promoting more equitable access to pharmaceuticals, especially to most disadvantaged groups, by expanding the offered benefits relative to other groups, and further reducing the financial burden of impoverishing health expenditures caused by drug costs. Declarations Funding Statement This study was funded by the Shota Rustaveli National Science Foundation of Georgia under grant number FR-22-7764. The sponsors were not involved in the study's design, data collection, analysis, interpretation of results, or the decision to publish. Author contribution statement Contributions: T.G., A.Z. and G.G. conceptualized the paper, selected a theoretical framework and methodology for the analysis. T.G. worked on descriptive and inferential analysis and implemented selected methodology to answer the research objectives. T.G performed the descriptive analysis and ran the initial regression analyses and diagnostic tests. G.G., T.G and A.Z reviewed and verified the analytical methods and obtained results. All authors contributed to the manuscript production: T.G. contributed to the descriptive analysis section. T.G., A.Z. and G.G. authored the introduction and background sections and supported the writing of ITS results. G.G. and A.Z contributed to interpreting the results. All authors provided critical input, shaping the research, analysis, and manuscript production. Competing interest statement All authors declare that they have no competing interests. Ethics approval Although this was secondary analysis of the publicly available anonymized datasets, the research team did obtain the ethical approval from the Health Research Union's ethical committee (protocol #2024-01, approved 04/03/2024). Data sharing statement The data used in this research is available from the GeoStat website: https://www.geostat.ge/en/modules/categories/128/databases-of-2009-2016-integrated-household-survey-and-2017-households-income-and-expenditure-survey. References Transforming our World. The 2030 Agenda for Sustainable Development | Department of Economic and Social Affairs [Internet]. United Nations; 2015 [cited 2024 Dec 26]. Available from: https://sdgs.un.org/publications/transforming-our-world-2030-agenda-sustainable-development-17981 WHO. Universal Health Coverage Overview [Internet]. [cited 2024 Dec 26]. Available from: https://www.who.int/health-topics/universal-health-coverage Medicines Selection IP, Affordability WHO. 2017 [cited 2024 Dec 26]. Available from: https://www.who.int/publications/m/item/access-to-medicines-making-market-forces-serve-the-poor Goginashvili K, Nadareishvili M, Habicht T. Can people afford to pay for health care? New evidence on financial protection in Georgia. [Internet]. Copenhagen: WHO Regional Office for Europe; [cited 2024 Oct 2]. Available from: https://www.who.int/europe/publications/i/item/9789289055802 Gorgodze T, Zoidze A, Catalan JM, Gotsadze G. Breaking the Cycle: Addressing the Drivers of Impoverishing Healthcare Costs in Georgia [Internet]. medRxiv; 2025 [cited 2025 Feb 7]. p. 2025.01.31.25321173. Available from: https://www.medrxiv.org/content/ 10.1101/2025.01.31.25321173v1 Rukhadze T. An overview of the health care system in Georgia: expert recommendations in the context of predictive, preventive and personalised medicine. EPMA J. 2013;4(1):8. Tsikhelashvili, et al. The economic transformation of Georgia in its 20 years of independence. European Initiative – Liberal Academy Tbilisi; 2013. World Bank. World Bank Gender Data Portal. [cited 2024 Dec 26]. GDP per capita (current US $ ). Available from: https://genderdata.worldbank.org/en/indicator/ny-gdp-pcap-cd Circle. Poverty and Gini Coefficients - National Statistics Office of Georgia [Internet]. [cited 2024 Dec 17]. Available from: https://www.geostat.ge/en/modules/categories/192/living-conditions Current health expenditure (CHE). as percentage of gross domestic product (GDP) (%) [Internet]. [cited 2024 Dec 17]. Available from: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/current-health-expenditure-(che)-as-percentage-of-gross-domestic-product-(gdp)-(- ). World Health Organization. Out-of-pocket expenditure as percentage of current health expenditure (CHE) (%) [Internet]. [cited 2024 Oct 3]. Available from: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/out-of-pocket-expenditure-as-percentage-of-current-health-expenditure-(che)-(- ). Curatio International Foundation. Barometer Study: Pharmaceutical Sector in Georgia [Internet]. 2024 [cited 2024 Oct 4]. Available from: https://curatiofoundation.org/barometer-study-pharmaceutical-sector-georgia/ On the approval of. the 2017 state health protection program. Legislateive herald of Georgia; 2017. On introducing changes to the 2017 state health protection programs. 2018 [Internet]. Legislative Herald of Georgia. 2018. Available from: https://www.gov.ge/files/496_67489_413376_430.pdf Legislative herald of Georgia. About the measures to be taken in order to transition to universal health care [Internet]. [cited 2024 Sep 17]. Available from: https://matsne.gov.ge/en/document/view/1852448 On determination of the list. of state bodies regulating pharmaceutical products of other countries or interstate [Internet]. Legislateive herald of Georgia; 2022 [cited 2024 Dec 19]. Available from: https://matsne.gov.ge/ka/document/view/5355246 On approval of. methodology, rules and conditions for state regulation of pharmaceutical product price [Internet]. Legislateive herald of Georgia; 2022 [cited 2024 Dec 19]. Available from: https://matsne.gov.ge/en/document/view/5666436 National Statistics Office of Georgia. Households Incomes and Expenditures [Internet]. 2024 [cited 2024 Sep 30]. Available from: https://www.geostat.ge/media/63659/Households-Incomes-and-Expenditures.PDF National Statistics Office of Georgia. Households Incomes and Expenditures Survey [Internet]. [cited 2024 Sep 30]. Available from: https://www.geostat.ge/en/modules/categories/128/databases-of-2009-2016-integrated-household-survey-and-2017-households-income-and-expenditure-survey Penfold RB, Zhang F. Use of interrupted time series analysis in evaluating health care quality improvements. Acad Pediatr. 2013;13(6 Suppl):S38–44. Li L, Cuerden MS, Liu B, Shariff S, Jain AK, Mazumdar M. Three Statistical Approaches for Assessment of Intervention Effects: A Primer for Practitioners. Risk Manag Healthc Policy. 2021;14:757–70. Alkhawaldeh A, ALBashtawy M, Rayan A, Abdalrahim A, Musa A, Eshah N, et al. Application and Use of Andersen’s Behavioral Model as Theoretical Framework: A Systematic Literature Review from 2012–2021. Iran J Public Health. 2023;52(7):1346–54. Lumley T, Hoboken R. N.J: Wiley; 2010. 276. (Wiley series in survey methodology). Circle. Population and Demography. - National Statistics Office of Georgia [Internet]. [cited 2024 Dec 12]. Available from: https://www.geostat.ge/en/modules/categories/316/population-and-demography Pavenik N. Do pharmaceutical prices respond to potential patient out-of-pocket expenses? Rand J Econ. 2002;33(3):469–87. Brekke KR, Grasdal AL, Holms TH. Regulation and pricing of pharmaceuticals: Reference pricing or price cap regulation? Eur Econ Rev. 2009;53(2):170–85. Iravani F, Mamani H, Nategh E. External Reference Pricing and Parallel Imports of Pharmaceuticals: A Policy Comparison. Prod Oper Manage. 2020;29(12):2716–35. Babar ZUD. A conceptual framework to build effective medicine pricing policies for low and middle-income countries (LMICs). Res Social Administrative Pharm. 2024;20(9):934–9. Hsu J, Jowett M, Mills A, Hanson K. What influences the impact of health financing reforms? Using qualitative comparative analysis to identify patterns in health financing systems and their effects on financial protection. SSM - Health Syst. 2025;100055. Lela Sulaberidze E. al. External Reference Pricing Policy: A Possible Pharmaceutical Price Regulation Policy in Georgia. Curatio International Foundation; 2022. Incze A, Kaló Z, Espín J, Kiss É, Kessabi S, Garrison LP. Assessing the Consequences of External Reference Pricing for Global Access to Medicines and Innovation: Economic Analysis and Policy Implications. Front Pharmacol [Internet]. 2022 Apr 6 [cited 2024 Dec 12];13. Available from: https://www.frontiersin.org/journals/pharmacology/articles/ 10.3389/fphar.2022.815029/full ERP-evidence-synthesis_20. 01.2022_ENG.pdf [Internet]. [cited 2024 Dec 12]. Available from: https://curatiofoundation.org/wp-content/uploads/2022/01/ERP-evidence-synthesis_20.01.2022_ENG.pdf Babaie F, Motevalli MH, Mehralian G, Peiravian F, Yousefi N. How does external reference pricing work in developing countries: evidence from Iran. Front Pharmacol. 2023;14:1034229. Additional Declarations No competing interests reported. Supplementary Files Supplement1..docx Cite Share Download PDF Status: Published Journal Publication published 02 Jun, 2025 Read the published version in International Journal for Equity in Health → Version 1 posted Editorial decision: Revision requested 24 Mar, 2025 Reviews received at journal 23 Mar, 2025 Reviews received at journal 04 Mar, 2025 Reviewers agreed at journal 25 Feb, 2025 Reviewers agreed at journal 23 Feb, 2025 Reviewers invited by journal 15 Feb, 2025 Editor assigned by journal 15 Feb, 2025 Submission checks completed at journal 12 Feb, 2025 First submitted to journal 07 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5981851","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414736566,"identity":"ff5357ca-3e2e-4cc3-99c0-832fbfdb069d","order_by":0,"name":"Tsotne Gorgodze","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFCDAwyMD8AMZgIKeZC0MBuARUjRwiaBKoID2PMfYH7xcU+tHN/5w8cqfrbdk7dnZz7A8KNiG25bJBLYLGc8O24seeBY2s3etmLDHma2BMaeM7fxaGFgM+Y5cCxxw8Ees9uMbUDVzDwGzIxteLTwH4BqOcz/rRioxZ6wFoYE5sc8B2oSNxzjYQOqTEgkrOVGYhvjjAMHjCXPsBlL9pxLSO45zJZwEJ9f2PsPH/7w4UAdKMQefvhRlmDb3n/44IMfFbi1MDAwtgGj4zCq2AE86kGA+QMDQx0BNaNgFIyCUTCiAQCwpFVoNulM1AAAAABJRU5ErkJggg==","orcid":"","institution":"Curatio International Foundation","correspondingAuthor":true,"prefix":"","firstName":"Tsotne","middleName":"","lastName":"Gorgodze","suffix":""},{"id":414736567,"identity":"0f6e5823-6497-4d7f-9369-5ac917b2fabd","order_by":1,"name":"Akaki Zoidze","email":"","orcid":"","institution":"Ilia State University, School of Natural Sciences and Medicine","correspondingAuthor":false,"prefix":"","firstName":"Akaki","middleName":"","lastName":"Zoidze","suffix":""},{"id":414736568,"identity":"4d00e28c-79e9-4038-9dc2-9e3e6d550a52","order_by":2,"name":"George Gotsadze","email":"","orcid":"","institution":"Curatio International Foundation","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Gotsadze","suffix":""}],"badges":[],"createdAt":"2025-02-07 14:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5981851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5981851/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12939-025-02535-x","type":"published","date":"2025-06-02T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76289674,"identity":"5eb188b1-f6d8-4621-bf73-a41bbd0f4ce0","added_by":"auto","created_at":"2025-02-14 11:56:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13859,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of drug reimbursement plan budget and beneficiary population\u003c/p\u003e\n\u003cp\u003eSource: NHA, 2024\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5981851/v1/63f547709210d7ea2f7f0dc8.png"},{"id":76290112,"identity":"85d9ad3c-4396-41a2-8204-57bccb8dbd64","added_by":"auto","created_at":"2025-02-14 12:04:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59349,"visible":true,"origin":"","legend":"\u003cp\u003eInflation Index for healthcare groups 2017-2023\u003c/p\u003e\n\u003cp\u003eSource: CIF, Inflation on healthcare with a focus on pharmaceuticals, 2024\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5981851/v1/0be53f2c1a25160cfb8999bd.png"},{"id":76289673,"identity":"007f4898-c697-49a8-b48c-261aeb0e6dbe","added_by":"auto","created_at":"2025-02-14 11:56:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9786,"visible":true,"origin":"","legend":"\u003cp\u003eShare of OOP spending on health care spending groups\u003c/p\u003e\n\u003cp\u003eSources: HIES, NHA, GeoStat, 2024\u003c/p\u003e","description":"","filename":"Onlinedrawingimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5981851/v1/b7e8ed43ef3f85a5f306e2c1.png"},{"id":76290109,"identity":"9ef8bd7c-753f-4c30-8bfe-a2dfb223cb1f","added_by":"auto","created_at":"2025-02-14 12:04:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130689,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe influences of policy changes on the HHs median monthly expenditure on drugs over the study period\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5981851/v1/028929bc0258218e9ff2ecaa.png"},{"id":84242580,"identity":"749fda2b-010a-4e7e-9e77-9533a36812b3","added_by":"auto","created_at":"2025-06-09 16:09:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":922008,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5981851/v1/4906413d-c61e-40e0-8fd9-d3bbdb104254.pdf"},{"id":76290110,"identity":"58ad2ccf-43be-4ef3-a41f-201eb19c97b8","added_by":"auto","created_at":"2025-02-14 12:04:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":101194,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1..docx","url":"https://assets-eu.researchsquare.com/files/rs-5981851/v1/ae8a807b3b875d93d5e37932.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Counting the Savings: Impact of Georgia's Drug Policy Interventions on Households ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccess to essential medicines is crucial in achieving the Sustainable Development Goals (SDGs) - \u0026quot;ensuring access to safe, effective, quality, and affordable essential medicines and vaccines for all\u0026quot; (1). They are also fundamental to achieving Universal Health Coverage (UHC) objectives, which guarantees everyone access to health services, including essential medicines, without the risk of financial hardship (2). Yet according to WHO estimates, up to 90% of people in low- and middle-income countries (LMICs) purchase medicines through out-of-pocket payments (OOPs). For some households, this expense may require selling an asset, such as a family cow, or pulling children out of school, ultimately pushing the family deeper into intergenerational poverty (3).\u003c/p\u003e\n\u003cp\u003eGeorgia presents a particularly compelling case on this matter, as the introduction of the Universal Health Coverage Program (UHCP) in 2013 expanded coverage to nearly 90% of the population to promote equitable access to healthcare for everyone. Despite this, among the poorest households facing catastrophic spending, outpatient medicines accounted for 90% of OOP payments for health services in 2018, compared to just 24% among the wealthiest households. Additionally, over the years, medicines have remained the most significant component of OOP payments, comprising 69% in 2018 \u0026nbsp;(4). More recent studies have shown that over the years, households reporting OOP expenditure on drugs were 43 times more likely to experience impoverishing health expenditure (5). To address this issue, the Georgian government has implemented three major policies to reduce the burden of OOPs for medicines. These policy actions offer natural experiment for evaluating their impact and effectiveness. This study aims to assess the financial impact of three pharmaceutical policy actions in 2017-2023 on the population while offering valuable guidance for other LMICs striving to reduce out-of-pocket spending on pharmaceuticals through well-informed policy decisions.\u003c/p\u003e\n\u003cp\u003eThis study is part of a larger body of work that examines pharmaceutical policy and expenditures in Georgia (5) \u0026nbsp;(Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia\u0026rsquo;s Chronic Disease Medicine Program) and provides additional context. This paper specifically focuses on assessing the impact of the stated policy actions using an interrupted time series (ITS) model to better understand which policy actions might have had the most impact on reducing OOPs for medicines.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eGeorgia is a sovereign country located in the South Caucasus region of Eurasia. It is bordered by the Black Sea to the west, Russia to the north, Turkey and Armenia to the south, and Azerbaijan to the southeast (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e). Until 1991, as part of the Soviet Union, Georgia\u0026apos;s healthcare system operated under the Semashko model. After gaining independence and losing its 78% of GDP (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e), the country began transitioning away from the Semashko model and implementing various changes in the healthcare system. During this time, the economy experienced a significant upturn, and as of 2023, Georgia was classified as an upper-middle-income country with GDP per capita measured at 8,210 current US\u003cspan\u003e$\u003c/span\u003e (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e). Nevertheless, the benefits of economic growth are not shared equally across society, as poverty remains a significant issue. In 2023, 19.8 percent of the population lived below the relative poverty line, while the Gini coefficient stood at 0.36 (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOne of the most significant changes in healthcare policy was carried out in 2013, when Georgia\u0026apos;s newly elected government launched the UHCP, expanding public insurance coverage to over 90% of the population. Concurrently, current health expenditure (CHE) as a percentage of GDP saw a significant increase, reaching 7.26% in 2022, a figure relatively close to the WHO Europe region\u0026apos;s 8.06% for the same year (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e). However, a substantial portion of the CHE still comes from OOP payments, accounting for 40.45% in 2022, compared to the WHO European average of 26.79% for the same year (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe introduction of the UHCP marked a significant milestone in expanding healthcare access, but its limitations have highlighted persistent challenges. While the basic package of services covered under UHCP includes emergency care, outpatient services, elective surgery (with necessary examinations and diagnostics), cancer treatment, childbirth, infectious disease management, and certain medications for chronic conditions, its primary focus has been on inpatient and emergency treatment (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e). This prioritization has come at the expense of primary care and outpatient medicine coverage, leaving OOP spending on drugs as a substantial financial burden for households. Consequently, OOP drug expenses have become a major driver of healthcare costs, significantly increasing the likelihood of impoverishment, as shown in the recent analysis. Moreover, the limited pharmaceutical coverage and unregulated pricing have left patients vulnerable to escalating costs, further exacerbated by Georgia\u0026apos;s reliance on imported medicines, whose prices rose by 41% between 2016 and 2020 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo reduce the burden of OOP expenditure on drugs, the government of Georgia initiated multiple policy actions over the course of the years. In April 2017, the drug reimbursement scheme for chronic conditions was initiated, which focused on providing subsidized medicines for the population with the four most prevalent chronic conditions: hypertension, chronic obstructive pulmonary disease (COPD), diabetes (type 2) and chronic thyroid gland diseases. This scheme required beneficiaries to co-pay part of medicine prices; the state-covered amounts varied according to the individual\u0026rsquo;s socioeconomic status (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e). Over the years, both the number of beneficiaries and the program\u0026apos;s budget have consistently grown (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2018, the coverage was significantly extended to the retirement-age population and people with disabilities (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e). In 2019, the list of chronic conditions increased, incorporating the subsidized provision of drugs for the population with Parkinson\u0026rsquo;s disease and epilepsy with a 25% co-payment, (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e) leading to an increase in the number of beneficiaries and the allocated budget in subsequent years (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In July 2020, the drug benefits program for patients with chronic diseases underwent significant changes. Instead of being a separate budget sub-program, it was integrated into the UHCP, and centralized procurement and distribution of drugs managed by MoH, was substituted with drug reimbursement scheme through private retail pharmacies, which helped reduce geographical access barriers as well as shortcomings of central procurement and distribution (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e) (Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia\u0026rsquo;s Chronic Disease Medicine Program).\u003c/p\u003e\n\u003cp\u003eFigure 1 Evolution of drug reimbursement plan budget and beneficiary population\u003c/p\u003e\n\u003cp\u003eAnother significant policy action took place in January 2022, with the initiation of parallel drug imports from Turkey to introduce more affordable drug alternatives into the country (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e). Efforts to lower pharmaceutical prices continued in December 2022 with the implementation of External Reference Pricing (ERP) (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). The recent studies have indicated that the inflation index for medical products, appliances, and equipment declined in 2023 (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), most likely as a result of these measures (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e) .\u003c/p\u003e\n\u003cp\u003eFigure 2 Inflation Index for healthcare groups 2017-2023\u003c/p\u003e\n\u003cp\u003eDespite efforts to reduce OOP for pharmaceuticals, they continue to account for tht part of national drug expenditures, even with notable reductions in recent years (see Fig.\u0026nbsp;3), while for other services, the share of state funding is significantly higher. Therefore, evaluating household spending on pharmaceuticals in light of policy reforms remains essential to understanding and addressing the financial burden of out-of-pocket drug expenses in Georgia.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 3 Share of OOP spending on health care spending groups\u003c/em\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis analysis is based on pooled data from the 2015–2023 Household Income and Expenditure Surveys (HIES) conducted by Georgia's national statistical agency - GeoStat. These surveys employ a two-stage stratified cluster sampling method with census enumeration areas selected initially and followed by households within these areas. To account for regional and urban-rural differences, the sample is organized into 21 strata, with approximately 1,440 households randomly chosen each month. Every household is interviewed twice in two separate quarters within a year, and the same are revisited in the same quarters the following year to capture changes over time. After completing four rounds, about one-twelfth of the panel is replaced with the new households from the same cluster, helping maintain a consistent panel structure. The sampling frame is derived from the 2014 general population census, whereas before 2017, it was based on the 2002 census. Furthermore, until 2017, each household was surveyed consecutively across four quarters, leading to variations in data collection over time (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGeoStat provides free access to online microdata and survey documentation from its website (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The household-level dataset includes monthly consumption information categorized by the Classification of Individual Consumption by Purpose (COICOP) and a range of household characteristics. The individual member dataset captures demographic and socio-economic details, such as age, sex, and education of household members. For this study, the household-level data from the annual surveys conducted from 2015 to 2023 (excluding years 2017 and 2018) were combined into a single database (N: 88,332). Household-level characteristics were aggregated—either as attributes of the household head or as significant traits of at least one household member—for use in regression analysis. The dataset was refined to include only households with complete data on the relevant variables, yielding a final sample of 87,337 households (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThree modification points were identified to assess policy changes and their impacts on financial well-being. First, instead of choosing 2017 as a time point for policy action for state-subsidized medicines for chronic diseases, we eliminated data for 2017 and 2018 due to low budget spending and low uptake of this program (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, we selected 2019 as a time point for policy evaluation. For simplified pharmaceutical imports from Turkey we selected January 2022, and for the introduction of reference pricing December 2022 was chosen.\u003c/p\u003e\u003cp\u003eThe analysis was conducted in R (Version 2023.12.1 + 402) using an Interrupted Time Series (ITS) approach. The household-level database was aggregated monthly, with household median monthly pharmaceutical expenditure (identified by the COICOP classification) as the outcome variable. To control for inflation, all household health spending data (including for pharmaceuticals) was converted into constant 2015 January prices. Monthly aggregation helped enhance the model's predictive power. It allowed the examination of multiple intervention points related to pharmaceutical policy changes, whereas the median values adjusted for the highly positive skewed distribution (11.47) of monthly healthcare expenses on pharmaceuticals (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Socio-economic and health profile variables were incorporated into the final model to account for changes in population over time (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The selection of independent variables followed Andersen’s model of healthcare utilization (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and was based on data availability within the dataset. Categorical variables were aggregated monthly as the share of households exhibiting particular characteristics, while mean values were used for continuous variables after assessing distribution normality. Because we used median drug expenditure values that do not explicitly account for households with no pharmaceutical expenditure, using the dichotomous variable, we derived the share of households with drug expenditure for each month and included it in the model. Additionally, another dichotomous variable indicating whether households incurred expenditures on other healthcare services, such as inpatient, outpatient, dental, or diagnostic services, was included in the dataset. This variable was aggregated monthly as a share of households reporting such expenditures. The complex survey design was considered when creating the time series using the \"srvyr\" package (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e): household weights were used as sampling weights, stratum as the strata variable, and unique household identification numbers were used to account for the panel design.\u003c/p\u003e\u003cp\u003eThe final model was tested for seasonality by visually decomposing the outcome variable to assess periodic patterns over time. The visual inspection revealed a clear seasonal pattern addressed by incorporating Fourier terms into the model (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Additionally, the autocorrelation of residuals was tested using the Durbin-Watson statistic, and no significant autocorrelation was detected, as indicated by the p-value exceeding the acceptable threshold (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe descriptive analysis of the study sample revealed that most households did not have children under the age of 6 as members, while the majority included at least one elderly member. Furthermore, most households reported having at least one chronically ill member. Notably, around 70% of households reported expenditures on drugs over the years, making this the more frequently reported type of healthcare expenditure compared to all other healthcare services combined. As expected, households with at least one elderly member and at least one chronically ill member had higher monthly median expenditures on drugs. The spending was even higher for households consisting of at least one person with a disability as a member. Additionally, the vast majority of household heads reported having either public or private health insurance, indicating high population coverage with prepayment schemes, mostly attributed to UHCP.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the analytical sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted count of HHs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted count of HHs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% of all HHs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInflation adjusted Median monthly HH expenditure on drugs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87,337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,428,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,022,671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,037,894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,042,563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,061,698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,090,179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,070,849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,102,314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale headed HHs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57,796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,716,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale headed HHs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29,541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,712,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head without higher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48,098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,660,139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head with higher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,768,030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs without child members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,062,276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs with at least one child member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,365,893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH without elder members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31,206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,905,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH with at lease one elder member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,523,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban HHs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,889,586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural HHs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36,526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,538,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs in top 3 per capita expenditure quintiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49,388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,460,321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs in bottom 2 per capita expenditure quintiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37,949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,967,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs without a member with disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78,873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,776,944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs with at least one member with disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e651,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs without a chronically ill member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,670,946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs with at least one chronically ill member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57,781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,757,222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head younger than 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38,553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,549,423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head age older than 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48,784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,878,745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH with less than 3 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50,499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,166,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH with more than 3 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36,838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,261,894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs without additional healthcare expenditure on other services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,316,786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs with additional healthcare expenditure on other services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23,503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,111,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs without expenditure on drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,062,216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHs with expenditure on drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64,515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,365,953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head without a spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,491,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head with a spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,936,369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head without insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head with insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,411,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the regression analysis are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Intercept \u0026minus;\u0026thinsp;40.01 (95% CI: \u0026minus;\u0026thinsp;443.61: 363.58; p\u0026thinsp;=\u0026thinsp;0.85) indicates that the baseline household monthly spending level on medicines before any policy interventions was not significantly different from zero in our model. This suggests that baseline levels do not meaningfully explain household spending on medication over time. The coefficient for time (pre-intervention trend) was 0.08 (95% CI p\u0026thinsp;=\u0026thinsp;0.4), which was not statistically significant, suggesting that inflation-adjusted household drug spending in constant January 2015 prices did not exhibit any statistically significant trend prior to policy interventions.\u003c/p\u003e \u003cp\u003eThe policy intervention of 2017 measured starting from 2019 had a coefficient of 1.94 (95% CI: -3.51:7.38; p\u0026thinsp;=\u0026thinsp;0.488) and a trend estimate of -0.17 (95% CI: -0.41 : 0.07; p\u0026thinsp;=\u0026thinsp;0.16), both statistically insignificant, indicating no notable impact on adjusted household median spending on drugs, possibly because still relatively low uptake of benefits by population and low financial limits paid by the government (see more about Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia\u0026rsquo;s Chronic Disease Medicine Program).\u003c/p\u003e \u003cp\u003eFor the second policy change in 2022, which simplified the parallel import of drugs from Turkey, the immediate effect was measured at -2.26 (95% CI: -7.39: 2.88; p\u0026thinsp;=\u0026thinsp;0.392), while the overtime trend was measured at 1.43 (95% CI: 0.75: 2.12; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that the intervention did not initially reduce spending but was associated with a statistically significant post-intervention effect, leading to an increase in household drug spending over time.\u003c/p\u003e \u003cp\u003eFinally, only the introduction of external reference pricing of 2023 (intervention 3), with a statistically significant immediate effect estimate of -6.96 (95% CI: -12.48: 1.45; p\u0026thinsp;=\u0026thinsp;0.016) and trend estimates after the intervention of -1.28 (95% CI: -2.09: -0.48; p\u0026thinsp;=\u0026thinsp;0.003) with relatively high statistical significance, showed a significant immediate reduction by 29% in household drug median spending, with a continuing significant decline over time (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests that external reference pricing was the most effective policy for reducing household spending on drugs, possibly due to successful price-control measures and adjustments in drug coverage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003eThe influences of policy changes on the HHs median monthly expenditure on drugs over the study period\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn this model, most of the other variables, including household structure and income levels, were not statistically significant, indicating limited influence on adjusted drug spending. However, the mean age of household heads had a statistically significant coefficient of 1.65 (95% CI: 0.15: 3.14; p\u0026thinsp;=\u0026thinsp;0.03), suggesting that higher drug spending was associated with older household heads. This is likely due to the increased healthcare needs due to widespread chronic conditions that require medications for treatment. Additionally, as expected, the control variable indicating the share of households encountering expenditure on drugs showed a significant effect of 0.42 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.014) on median monthly expenditure.\u003c/p\u003e \u003cp\u003eFinally, statistically significant seasonal or cyclical variation in drug spending, measured with Fourier terms 1.14 (95% CI: 0.32: 1.97; p\u0026thinsp;=\u0026thinsp;0.001), confirms that changing drug spending was due to seasonal changes in healthcare needs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterrupted time series results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower_CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper_CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP_Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(40.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(443.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e363.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime after intervention 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(7.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime after intervention 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(6.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(12.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0161 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime after intervention 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0027 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of Female headed HH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of HH heads without higher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of HH with children under 6 as members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of HH with elder members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of rural HHs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of the poorest and 2nd poorest hhs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of HH s with members with disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of HH with chronically ill members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Age of HH heads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0348 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean HH size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(12.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of HH heads without a spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of Insured HH heads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of hhs encountering expenditure on healthcare services excluding drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of hhs encountering expenditure on drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0138 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourier terms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0087 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificance level:. \u0026lt;0.1, *\u0026lt;0.05, **\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results indicate that the external reference pricing (ERP) - third policy intervention was effective at reducing household spending on pharmaceuticals, both immediately and in the longer term. Household characteristics, such as age and drug expenditure patterns, also influence spending levels, highlighting the role of demographic factors. The model\u0026rsquo;s high R\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 0.76 value further underscores the model\u0026rsquo;s effectiveness in explaining spending variations. These insights could be valuable for policymakers in assessing the long-term impact of policy interventions on pharmaceutical spending and refining future interventions to sustain reductions in household financial burden.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eBefore discussing the results, we first would like to note the limitations of the study:\u003c/p\u003e \u003cp\u003eTo evaluate interventions using ITS, it is recommended to have at least 12 time points before and after the intervention (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, in the case of the second policy change, which focused on allowing parallel drug imports from Turkey, only 11 time points were available after its implementation and prior to the third assessed policy. This may impact the reliability of findings related to this policy action. The same argument can be made regarding evaluating the isolated impact of policy modifications related to chronic disease medicine program. The drug reimbursement program underwent an evaluation described elsewhere (Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia\u0026rsquo;s Chronic Disease Medicine Program). Government spending gradually rose from 2017 to 2023 (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); however, these annual changes cannot be integrated into the model due to their monthly variability and the absence of official documentation detailing when specific budget adjustments were made. Moreover, excluding the years 2017 and 2018 from the model may have introduced some bias in measuring the overall trend before policy implementation. While it is unlikely to have impacted the coefficients for policy change effectiveness, it remains a relevant consideration.\u003c/p\u003e \u003cp\u003eSecond, due to the absence of a specific utilization variable in the dataset, expenditure on health was used as a proxy to account for health service utilization. We do not view this as a significant limitation of the study, however, it is important to acknowledge.\u003c/p\u003e \u003cp\u003eFinally, the impact of ERP may overlap with that of the Chronic drug reimbursement scheme, introduced in 2017, which took some years to pick up a pace and reach a higher number of beneficiaries in 2023. Therefore, the gradual evolution of this program and the benefits afforded to the population (Tsuladze A, Zoidze A, Kotrikadze N, Stauke J, Gotsadze G: Breaking Barriers to Universal Health Coverage: Insights from Georgia\u0026rsquo;s Chronic Disease Medicine Program) could have overlapping effects with the ERP, and the separation of two policy effects on monthly drug expenditure was not possible. Nonetheless, drug benefits did reach 276,977 beneficiaries (or 7.4 of the population %) before the end of 2021, but its effects were not visible in our analysis. .However, the steady increase in government spending on pharmaceuticals, particularly in recent years, suggests that the positive effects on drug expenditure may not be attributable to the ERP alone.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe increased budges in drug reimbursement plan in 2019 did not significantly impact the population's out-of-pocket (OOP) spending on medications. Several factors likely contributed to its limited effectiveness: first, the program's budget, particularly in 2019 and 2020 was significantly lower than the overall OOP expenditure on medicines the population bears. Second, the number of beneficiaries in 2019 was insignificantly lower \u0026ndash; 145,279 individuals compared to 473,048 (or 17% of the adult population) in 2023 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Finally, the plan's limited coverage, focusing only on a restricted list of chronic conditions and with financial limits for reimbursement, was also insufficient to offset OOP spending with government contributions.\u003c/p\u003e \u003cp\u003eSignificant progress was made with implementing the drug reimbursement program since 2020, when the program was moved to UHCP and budget allocations were increased and financial limits for reimbursement expanded. However, expanding the number of beneficiaries and extending the benefits took several years. The first major growth after 2019 was observed in 2021, with 276,977 beneficiaries, followed by substantial increases in 2022 and 2023, reaching 473,048 beneficiaries receiving drug benefits in 2023. Despite these advancements, the program's impact on the wellbeing of the population remained limited, as drug spending remained to be a major determinant of impoverishment even in 2023 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). While the program did not provide financial protection to the households' it positively affected the balance between the government\u0026rsquo;s share of spending on drugs. Because drugs still remain a major impoverishing expense for households, a further increase in government spending on drugs seems necessary.\u003c/p\u003e \u003cp\u003eNext, considering the limitations noted earlier, we did not observe any positive changes accompanying parallel imports from Turkey. However, other studies that looked at possible links between simplified imports from Turkey in 2022 and drug expenditure noted reductions in price growth (negative inflation) on medications (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), nonetheless, this positive effect was not confirmed in our model. Nevertheless, this finding should be interpreted cautiously due to the model's limitations, particularly its failure to meet the minimum requirement of 12-time points, resulting in insufficient statistical power. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe external reference pricing introduced in 2023 had the most pronounced effect in reducing average household monthly spending on medicines. Marked by a one-time drop in pharmaceutical expenditure by 6.96 Gel (95% CI:-12.48:- 1.45) followed by a sustained monthly decline by 1.28 Gel (95% CI:-2.09:-0.48) in constant 2015 January prices. Without this policy, Georgia's population would have spent an additional 43.44 43.3 (95% CI: 18.0: 68.87) million GEL (in nominal 2023 GEL) on pharmaceuticals over 12 months in 2023. The fiscal impact of the reform can be attributed to two factors: first, the broad reach of the reference pricing policy, which affected all segments of society and every household that incurred expenses on medicines (74% of the households in 2023); and second, the expansion of state-subsidized medicine beneficiaries in 2023 which reached 17% of the adult population (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Therefore, as noted in the limitations, the impact likely represents the combined effects of both policy actions, making it challenging to determine the isolated impact of each policy action alone. Nonetheless, existing literature emphasizes the strong potential of ERP reforms to reduce pharmaceutical costs (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). And comparable positive effect on controlling pharmaceutical costs has been documented in low- and middle-income countries (LMICs) elsewhere (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Moreover, the findings of our study suggesting positive influence on household\u0026rsquo;s financial protection from drug expenditures being a result of a cumulative effect of two or more policy interventions studied that targeting different attributes of health financing system (revenues rising, pooling and purchasing) is consistent with the recent systematic literature review, which argues that while a combination of policy interventions targeting more than one attribute is needed for positive impact on financial protection (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study uncovered positive results that can be plausibly linked to ERP policy and drug reimbursement schemes introduced in Georgia. However, other countries' experiences indicate that ERP policy's benefits may evaporate over time (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). According to the review of international experience, as ERP policies become more widely used, it may drive manufacturers to adopt counterstrategies, such as imposing narrow list price corridors or delaying/avoiding product launches in a given geography, which could exacerbate geographic inequities in access to new products (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Therefore, Georgia, learning from the experiences in other countries, has to closely monitor the price movements and their impact on the population and, when necessary, should consider revising and updating the ERP policy to achieve more favorable outcomes for its citizens (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Furthermore, ERP reform should be combined with other regulatory and policy measures for optimal results (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This appears to be the case in Georgia, where the study's results likely reflect the combined impact of changes in drug reimbursement plan and the ERP and the country may need to further enhance drug benefits program. Such enhancement could entail improving program effectiveness, promoting more equitable access to pharmaceuticals, especially to most disadvantaged groups, by expanding the offered benefits relative to other groups, and further reducing the financial burden of impoverishing health expenditures caused by drug costs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Statement\u003c/h2\u003e\n\u003cp\u003eThis study was funded by the Shota Rustaveli National Science Foundation of Georgia under grant number FR-22-7764. The sponsors were not involved in the study\u0026apos;s design, data collection, analysis, interpretation of results, or the decision to publish.\u003c/p\u003e\n\u003ch2\u003eAuthor contribution statement\u003c/h2\u003e\n\u003cp\u003eContributions: T.G., A.Z. and G.G. conceptualized the paper, selected a theoretical framework and methodology for the analysis. T.G. worked on descriptive and inferential analysis and implemented selected methodology to answer the research objectives. T.G performed the descriptive analysis and ran the initial regression analyses and diagnostic tests. G.G., T.G and A.Z reviewed and verified the analytical methods and obtained results.\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the manuscript production: T.G. contributed to the descriptive analysis section. T.G., A.Z. and G.G. authored the introduction and background sections and supported the writing of ITS results. G.G. and A.Z contributed to interpreting the results. All authors provided critical input, shaping the research, analysis, and manuscript production.\u003c/p\u003e\n\u003ch2\u003eCompeting interest statement\u003c/h2\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eAlthough this was secondary analysis of the publicly available anonymized datasets, the research team did obtain the ethical approval from the Health Research Union\u0026apos;s ethical committee (protocol #2024-01, approved 04/03/2024).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData sharing statement\u003c/h2\u003e\n\u003cp\u003eThe data used in this research is available from the GeoStat website: https://www.geostat.ge/en/modules/categories/128/databases-of-2009-2016-integrated-household-survey-and-2017-households-income-and-expenditure-survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTransforming our World. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://curatiofoundation.org/wp-content/uploads/2022/01/ERP-evidence-synthesis_20.01.2022_ENG.pdf\u003c/span\u003e\u003cspan address=\"https://curatiofoundation.org/wp-content/uploads/2022/01/ERP-evidence-synthesis_20.01.2022_ENG.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabaie F, Motevalli MH, Mehralian G, Peiravian F, Yousefi N. How does external reference pricing work in developing countries: evidence from Iran. Front Pharmacol. 2023;14:1034229.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5981851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5981851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction\u003c/p\u003e\n\u003cp\u003eAccess to essential medicines is fundamental to achieving the Sustainable Development Goals and Universal Health Coverage. However, in Georgia, as in other low and middle-income countries, out-of-pocket payments for medicines remain a significant financial burden, particularly for low-income households. Despite implementing the Universal Health Coverage Program in 2013, medicines account for the largest share of OOP health expenditures, exacerbating the risk of impoverishment. This study uses interrupted time series analysis to evaluate the impact of four major pharmaceutical policy interventions initiated between 2017 and 2023 on household monthly drug expenditures.\u003c/p\u003e\n\u003cp\u003eMethodology\u003c/p\u003e\n\u003cp\u003eThe analysis utilized pooled data from the 2015–2023 Household Income and Expenditure Surveys, covering over 110,000 households. Monthly median drug expenditures were adjusted to constant prices in January 2015 and analyzed. Three policy interventions were assessed: the 2017 drug reimbursement plan, the introduction of parallel imports from Turkey in 2022, and the implementation of external reference pricing in 2023. The regression models accounted for seasonality and complex survey design features, including weights and clustering.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eThe results demonstrated that only the external reference pricing policy led to an immediate reduction of 6.96 GEL (p = 0.016) and a sustained monthly decline of 1.28 GEL/month (p = 0.002), representing a 29% reduction in monthly spending, which saved households an estimated 43.3 (95 % CI: 18.0: 68.9) million GEL in 2023. The 2022 parallel import policy showed an immediate decrease of 2.26 GEL (p = 0.39) but was followed by a significant upward trend (coefficient = 1.43, p \u0026lt; 0.001). Budget modifications earlier intervention in 2019 did not yield significant changes in spending levels or trends.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eThese findings emphasize the impact of external reference pricing in reducing the financial burden of pharmaceutical expenditures and underscore its potential as a viable policy option for other low—and middle-income countries. Nevertheless, based on the experience of different countries, sustained reductions in household spending on drugs necessitate ongoing monitoring and supplementary measures to address disparities in access and ensure a lasting, positive impact on the population. Experiences in Georgia could be educational for policymakers in low-middle-income settings.\u003c/p\u003e","manuscriptTitle":"Counting the Savings: Impact of Georgia's Drug Policy Interventions on Households ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 11:55:58","doi":"10.21203/rs.3.rs-5981851/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-24T07:55:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-24T00:11:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-04T19:48:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302205657505367621866236988418700743769","date":"2025-02-25T14:55:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149582603785235364021966422922155819078","date":"2025-02-23T19:46:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-15T21:21:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-15T21:15:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-12T11:29:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Equity in Health","date":"2025-02-07T13:57:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e24a3019-bae7-4ca3-ab1b-24cdf37c5d87","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:02:28+00:00","versionOfRecord":{"articleIdentity":"rs-5981851","link":"https://doi.org/10.1186/s12939-025-02535-x","journal":{"identity":"international-journal-for-equity-in-health","isVorOnly":false,"title":"International Journal for Equity in Health"},"publishedOn":"2025-06-02 15:57:31","publishedOnDateReadable":"June 2nd, 2025"},"versionCreatedAt":"2025-02-14 11:55:58","video":"","vorDoi":"10.1186/s12939-025-02535-x","vorDoiUrl":"https://doi.org/10.1186/s12939-025-02535-x","workflowStages":[]},"version":"v1","identity":"rs-5981851","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5981851","identity":"rs-5981851","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}