For analysis of prices, the unit prices of each surveyed OADs, defined as price per capsule or tablet, were considered. The unit prices were calculated using the following equation: U n i t   p r i c e = P r i c e   o f   p a c k a g e   o f   m e d i c i n e   f o u n d P a c k   s i z e   o f   m e d i c i n e   f o u n d

The prices were reported as Median Unit Prices (MUPs) categorized by medicine, price type (LPG, HPG, OB), NEML status (NEML and non-NEML medicines), and formulation type (SAI and FDC). The three price types were compared across two primary grouping variables, NEML status, and formulation type, before and after the deregulation policy using the Wilcoxon rank-sum test. Statistical significance was determined at a p-value threshold of 0.05. Observations with missing price data were excluded to ensure accuracy in comparisons. This comparative analysis was performed to set a foundation for the subsequent Difference-in-Differences (DiD) analysis to assess the impact of deregulation policy.

DiD analysis, a quasi-experimental approach, was applied to evaluate the effects of price deregulation on medicine prices in Pakistan. It is a widely recognized method to measure the impact of health policies (Zhang et al., 2022b; Teng et al., 2024). The analysis compared price changes in NEML medicines (control group/unexposed to deregulation) and non-NEML medicines (treatment group/exposed to deregulation policy), before and after the deregulation policy’s implementation. The data included unit prices of both NEML and Non-NEML OADs across three pricing categories (LPG, HPG, and IB), pre and post price deregulation policy. In the DiD model, we specified three regression equations. For LPG : L P G p r i c e = β o + β 1 P o s t d e r e g u l a t i o n + β 2 N E M L s t a t u s + β 3 P o s t d e r e g u l a t i o n × N E M L s t a t u s + ϵ For HPG : H P G p r i c e = α o + α 1 P o s t d e r e g u l a t i o n + α 2 N E M L s t a t u s + α 3 P o s t d e r e g u l a t i o n × N E M L s t a t u s + ϵ For IB : I B p r i c e = γ o + γ 1 P o s t d e r e g u l a t i o n + γ 2 N E M L s t a t u s + γ 3 P o s t d e r e g u l a t i o n × N E M L s t a t u s + ϵ

In each equation, β o , α o , and γ o represent the intercepts or base price levels; β 1 , α 1 , and γ 1 capture the effect of the deregulation policy on unit prices; β 2 , α 2 , and γ 2 indicate price differences based on NEML status; and, β 3 , α 3 , and γ 3 represent the interaction term, estimating the difference-in-differences effect, or the relative price change of Non-NEML medicines compared to NEML medicines post-deregulation. To guarantee consistency, robust standard errors were fitted to DiD regression models. The robustness of the model was validated by sensitivity studies, which included a placebo test.

The formulation type variable was not included in the DiD analysis because all of the FDCs were non-NEML i.e., the exposed group. So, the price changes across FDCs and SAIs after deregulation were assessed separately through linear regression models. To analyze whether the price changes varied between these groups, interaction terms were also included, as per the following general model: Y i j = β o + β 1 P e r i o d j + β 2 F D C i + β 3 P e r i o d j × F D C i + ϵ

Where, Y i j is the unit price of medicine i in period j (e.g., LPG, HPG, or OB prices). The constant term β o reflects the baseline average price for SAI medicines in the pre-deregulation period. β 1 captures the price change for SAI medicines between pre- and post-deregulation periods, while β 2 represents the baseline price difference between FDCs and SAI medicines. The interaction term coefficient β 3 estimates the differential impact of deregulation on FDCs compared to SAI medicines, with ϵ as the error term.

The relationship between the number of registered brands and pre-post prices (including LPG, HPG, and OB) was also evaluated through linear regression models.

The affordability of surveyed OADs was calculated in terms of the number of days’ wages (NDWs) required for a lowest-paid government employee to afford the standard treatment course. The defined daily dose (DDD) of each OAD, which is the “assumed average maintenance dose per day for a drug used for its main indication in adults,” was used as a standard dose unit of measurement in the analysis of affordability for standard treatment of each medication (Deressa et al., 2024). The monthly treatment cost and NDWs were calculated by using the following equations: T r e a t m e n t   c o u r s e   c o s t = N u m b e r   o f   u n i t   d o s e   r e q u i r e d   f o r   D D D   o f   O H G A x   M e d i a n   u n i t   p r i c e   o f   O H G A x d a y s   o f   t r e a t m e n t N u m b e r   o f   d a y s ′ w a g e s   r e q u i r e d =   T r e a t m e n t   c o u r s e   c o s t   l o c a l   c u r r e n c y P e r   d a y   w a g e   o f   l o w e s t   p a i d   g o v e r n m e n t   w o r k e r   l o c a l   c u r r e n c y

The salary taken for the analysis was 32000PKR per month, i.e., 1066 PKR or 3.76 USD per day (with effect from July 2023). Notably, this threshold closely aligns with the World Bank’s lower-middle-income poverty line of $3.65/day (2017 PPP)–a metric encompassing 39.4% of Pakistan’s population in fiscal year 2023–24 (World Bank, 2023). According to the standard WHO/HAI methodology, the medicine is considered affordable only if the lowest-paid unskilled government employee spends less than 1 day’s wage to get the standard treatment from that medicine (Health Action International, 2018). The Wilcoxon signed-rank test was used to compare the affordability of surveyed drugs, before and after the deregulation policy, by stratifying the data into two categorical variables—NEML status and formulation type. Descriptive statistics were computed for each variable.

Availability was reported in two categories OBs and generics (LPG/HPG). It was calculated as the percentage of a particular medicine available at each facility on the day of data collection, using the following equation: A v a i l a b i l i t y % = N u m b e r   o f   f a c i l i t i e s   w i t h   t h e   m e d i c i n e   a v a i l a b l e T o t a l   n u m b e r   o f   f a c i l i t i e s   s u r v e y e d x 100

Only post-deregulation availability data were considered in the analysis. The percentage availability was categorized as follows: absent, indicating that 0% of facilities surveyed had the enlisted medicines at the time of the survey; low, indicating that less than 50% of facilities had the surveyed enlisted medicines; fairly high, indicating that 50%–80% of facilities had the surveyed enlisted medicines; high, indicating that more than 80% of facilities had the surveyed enlisted medicines, with them being found in most of the facilities (Saeed et al., 2019). To compare the availability of OADs, we conducted a series of two-sample t-tests with equal variances, comparing mean availability across different groups categorized by NEML status, price category, and formulation type.

The top five OADs with the highest increase in MUPs were Rosiglitazone 4 mg Tabs (LPG: 296.23%), Glipizide 5 mg Tabs (LPG: 174.05%), Gliclazide 80 mg Tabs (LPG: 63.65%; HPG: 87.76%), Glimepiride/Pioglitazone 4mg/15 mg Tabs (LPG: 88.78%; HPG: 131.02%), and Vildagliptin 50 mg Tabs (OB: 70.04%) (See Table 1; Supplementary Table S1).

In Figure 1, the box plot illustrates the distribution of MUPs for OADs categorized by price types: LPG, HPG, and OB, before and after the deregulation policy. The MUPs for surveyed medicines showed an upward trend across all price categories. For LPGs, the MUP rose moderately from PKR 22.43 to PKR 25.71. HPGs showed an increase in MUPs from PKR 35 to PKR 37.36. For OBs, the MUP showed a marked rise from PKR 21 pre-deregulation to PKR 30.80 post-deregulation. A significant change in price distribution reflected by the IQRs widening following deregulation underscores a wider dispersion of prices across all price types. Supplementary Table S2 provides the summary statistics of MUPs for OADs by price category pre- and post-deregulation.

Overall, the NEML medicines were priced lower than non-NEML medicines both before and after deregulation (p < 0.05). Details on the medians and statistical comparison of MUPs of OADs by NEML status, price, and formulation types, before and after deregulation, can be found in Supplementary Table S3. Interestingly, the price trend for NEML medicines was HPG > LPG > OB, whereas for non-NEML medicines the order reversed to OB > HPG > LPG. The MUPs of non-NEML OBs remained stable at PKR 59.80 post-deregulation, while MUPs of LPGs increased from PKR 26.5 to PKR 30.36, and HPGs rose from PKR 37.7 to PKR 39.29. In contrast, for NEML OBs, the MUPs increased modestly from PKR 13.56 PKR to PKR 15.51, whereas LPGs increased from PKR 18 PKR to PKR 20.39, and HPGs rose from PKR 24.29 to PKR 35. Notably, the average number of products registered for NEML and non-NEML medicines was 37 and 9, respectively.

The results of DiD analysis confirmed statistically significant price increases for all categories following deregulation (See Table 2). The biggest hike was observed in OBs (PKR 15.05, p = 0.004), followed by HPGs (PKR 5.97, p = 0.02) and LPGs (PKR 2.91, p = 0.01), suggesting that all prices were increased for these categories within 6 months of the policy’s introduction. It also confirmed that the NEML medications were consistently less expensive than non-NEML medications. The price differences between NEML and non-NEML medicines (in PKR) for OBs, HPGs, and LPGs were −30.20 (p < 0.001), −9.83 (p < 0.001), and −7.51 (p < 0.001), respectively, highlighting the protective function of NEML inclusion in preserving affordability. The interaction term (Period × NEML status), expressing the differential price effect of deregulation between NEML and non-NEML medicines, revealed no statistically significant difference in costs between NEML and non-NEML medications for LPGs (β = 0.77, p = 0.35) and HPGs (β = −0.20, p = 0.95) in PKR, after controlling for baseline differences. OBs, on the other hand, a somewhat greater and a marginally significant negative interaction was noted (β = −10.85, p = 0.05), indicating that NEML OB prices rose 10.85 units less after deregulation than non-NEML OB prices. These results suggest that, in contrast to their NEML counterparts, non-NEML OB prices were more significantly raised by the price deregulation, although the effects were less noticeable for LPGs and HPGs. Notably, the lower R-squared values suggest other unmeasured and important factors might have affected the medicine prices.

Upon stratification by formulation type, the Wilcoxon rank-sum test revealed the prices of LPG and OB of FDCs were significantly higher than those of SAI medicines, while for HPG FDCs the difference was not significant (p < 0.01), both before and after deregulation (See Supplementary Table S3). This trend was further confirmed by the regression results (Table 3), where the baseline unit prices of FDCs were considerably higher than SAI medicines with a difference of PKR 3.05 (p = 0.0009) for LPG, PKR 2.88 (p = 0.09: Not significant), and PKR 14.66 (p < 0.001). The findings also revealed that the prices of both SAI medicines and FDCs increased significantly for all price types (LPG, HPG, and OB) after deregulation. The interaction terms between formulation type and period (pre-post-deregulation) indicated no significant differential impact of deregulation on FDCs compared to SAI medicines across all price types.

The findings from the linear regression models revealed a significantly negative association between prices and the number of registered brands (reflecting market competition), both before and after deregulation (See Supplementary Table S4). For LPGs, the addition of each registered brand was associated with a decrease in unit price by PKR 0.26 pre-deregulation and PKR 0.20 post-deregulation (p < 0.001). For HPGs, the unit prices were reduced by PKR 0.33 and PKR 0.39 pre and post-deregulation (p < 0.001). OBs presented a relatively greater reduction in unit prices PKR 0.52 (p < 0.001) before deregulation and PKR 0.72 (p < 0.001) after deregulation.

All available price types of 7 OADs–Sitagliptin 100 mg Tabs, Vildagliptin 50 mg Tabs, Empagliflozin 10 mg Tabs, Dapagliflozin 5 mg Tabs, Pioglitazone 15 mg Tabs, metformin/sitagliptin 500mg/50 mg Tabs, Vildagliptin/metformin 50 mg/1 g Tabs–remained consistently unaffordable both before and after deregulation. Interestingly, two of these medicines, Empagliflozin 10 mg Tabs and dapagliflozin 5 mg Tabs are included in both NEML and WHO EML 2023. Whereas, the generic products (LPG and HPG) of three OADs–metformin/sitagliptin 1g/50mg, Glimepiride/Pioglitazone 4mg/15mg, and Metformin/Glimepiride 500mg/2 mg–jumped from the affordable to the unaffordable category. Supplementary Table S5 provides the NDWs for each medicine before and after the price deregulation policy.

The analysis revealed a significant increase in the NDWs for all price categories post-deregulation. The highest rise in median NDWs was noted for HPGs, increasing significantly by 31.65% from 0.79 to 1.04 (p < 0.001), bringing this category from affordable to unaffordable OADs. Although LPGs remained affordable but experienced a significant 19.40% rise in NDWs (p < 0.001). In contrast, the OBs exhibited minimal change in affordability by 6.89%, with median NDWs rising from 1.16 to 1.24 (p < 0.01). Although the change in the affordability of OBs was minimal, it is noteworthy that OBs remained unaffordable both before and after deregulation with median NDWs greater than 1 day.

The affordability of NEML LPG and HPG significantly worsened by 26.67% (p < 0.05) and 30.19% (p < 0.01) respectively. In contrast, the rise in median NDWs was minimal (7.55%) and insignificant for NEML OBs. The non-NEML medicines observed more pronounced rises in median NDWs by 18.99% for LPGs (p < 0.001), 15.84% for HPGs (p < 0.01), and 19.08% for OBs (p < 0.01). Specifically, the median NDWs rose from 0.79 to 0.94 for LPGs, 1.01 to 1.17 for HPGs, and 2.62 to 3.12 for OBs, keeping the non-NEML HPGs and OBs unaffordable. This analysis highlights the need for policies to address affordability issues, particularly for non-NEML OADs.

The median NDWs for SAI medicines increased significantly by 13.21% (p < 0.001) for LPG, 8.97% (p < 0.01) for HPG, and 8.86% (p < 0.05) for OB. The median NDWs of FDCs also increased post-deregulation, by 18.99% (p < 0.01) for LPG, 33.33% (p < 0.01) for HPG, and 27.18% (p > 0.05) for OBs. All price types of SAIs remained affordable i.e., NDW<1, before and after deregulation. In the case of FDCs, the LPG remained affordable, the HPG shifted from affordable to the unaffordable category, and OBs remained unaffordable before and after deregulation (See Table 4).

About two-thirds of the surveyed OADs demonstrated moderate to high availability, with rates ranging from 47% to 97%. Metformin 500 mg and Sitagliptin 100 mg were the two most accessible OADs with 97% availability. However, almost half of the OADs (n = 16) fell below the WHO benchmark for availability, set at 80%.

As per the results of independent t-tests to compare mean availability across different medicine categories, the LPGs exhibited significantly higher mean availability (57.85%) compared to OBs (27.42%), with a statistically significant difference of 30.43% (p < 0.05) (See Table 5).

The NEML medicines were found to be more available than the non-NEML medicines with 49% and 41.41% availability, respectively. However, this difference of 7.59 was insignificant (p = 0.535) (See Table 5). Seven of the 30 surveyed OADs had low availability, including one NEML medicine, Glipizide 5 mg Tabs (23%) (See Supplementary Table S6).

SAI formulations had a mean availability of 45.46%, which was slightly higher than FDCs at 38.72%. However, the difference of 6.75% was not statistically significant (p = 0.459).

Overall, the HPGs were found to be the most expensive, followed by OBs and LPGs, at both time points. Certain generics had higher prices than OBs, possibly due to high generic entrants in the Pakistani market. The results align with the findings by Baber et al., who reported that OBs of some antidiabetic medicines were cheaper than their LPGs in some LMICs, contrary to the observation in a majority of the countries where OBs were priced higher (Babar et al., 2019).

After deregulation, the prices rose significantly across all categories—LPG, HPG, and OB. Similar findings on the effect of deregulation policy on different categories of medicines, with the majority facing price upsurges were reported by Guan et al. and Costa et al. in the Chinese and Peruvian markets (Guan et al., 2019; Costa Font, 2016). Our results highlighted that the policy might have broadly impacted the market, leading to overall price hikes and a possible rise of monopolistic practices among the manufacturers (Zhang and Bian, 2023).

The deregulation policy targeted the non-NEML medicines, ideally, the prices of NEML medicines that were still under regulatory control should have stayed stable within this short period post-deregulation policy. However, the significant price surges of up to 296%, irrespective of their inclusion in the NEML 2023 translate into potentially inefficient implementation of the policy. Another contributing factor behind the lack of significant distinction between both NEML and non-NEML medicines could be the simultaneous price increments of NEML medicines e.g., prices of 146 essential drugs were increased in February 2024 alongside the launch of this deregulation policy for nonessential medicines (Azad, 2024). Nevertheless, the prices of NEML medicines remained lower than the non-NEML medicines at both time points. A multi-country study conducted by Bazarangi et al., also noted that essential medicines have significantly better accessibility than non-essential medicines (Bazargani et al., 2014). Notably, the OBs of NEML medicines were found to be high priced compared to generics while in the case non NEML medicines OBs were the most expensive. Reflecting the premium position of non-NEML OBs in the market. This disparity advocates for switching to the cheapest therapeutically equivalent alternative, which may lead to substantial cost savings (Abdullah et al., 2024).

The DiD analysis also confirmed that the NEML-listed medicines, preserved lower prices compared to non-NEML medicines, indicating the protective role of NEML against price hikes. However, this protective effect did not entirely shield essential medicines from price escalation potentially stimulated by deregulation. Liu et al. also detected higher prices for essential medicines after the introduction of the deregulation policy, gravely impacting low-income populations (Liu et al., 2017). The non-NEML medicines especially for highly prevalent diseases like diabetes, have become increasingly unaffordable. This may lead to poor access to innovative treatment options, essential for specific groups of people, thus affecting healthcare equity. The effects of price deregulation differed depending on the type of medication i.e., LPG, HPG, and OB. While LPGs and HPGs did not show any obvious differential effects, the non-NEML OBs had a comparatively larger and significant price increase following the policy than their NEML counterparts. This implies that medications not listed on the NEML may have been disproportionately impacted by price deregulation, especially OBs, which are frequently more expensive and might be less vulnerable to competition. These findings highlight the significance of specific regulatory protections for expensive, non-NEML medications in order to avoid issues with affordability in the private sector.

Increases in prices of both FDCs and SAI medicines were observed across all price categories (LPG, HPG, and OB) after the implementation of the deregulation policy. It was observed that all of the FDCs consistently commanded higher prices than SAI formulations across all price types before and after deregulation, reflecting their superior placement in the market (Hao et al., 2016).

The results highlight the significant impact of the number of registered brands representing the market competition on price drops among all categories, i.e., LPG, HPG, and OB. This effect was more prominent in OB prices, possibly due to higher price elasticity in this category. Nguyen et al. also noticed a 20% reduction in price for medicines with about three generic products in the market, and up to 80% price reductions for medicines with ten or more competitors (Nguyen et al., 2022). Chen Yina et al. also reported a price decline due to increasing market competition for drugs in China (Yina et al., 2023). The negative association between prices and market competition was more pronounced in post post-deregulation period, suggesting that the price deregulation policy might have augmented the role of market competition in bringing down the prices of OADs in the Pakistani market. The Peruvian market also showed lower prices for some medicines due to increased generic competition after the implementation of the deregulation policy (Costa Font, 2016). However, other factors related to market dynamics, including product-related, consumer-related, trading strategies, supply-related, and regulatory compliance, should also be considered to explore the impact of market competition along with deregulation on medicine prices (Borges dos Santos et al., 2019).