Abstract
Background Many Russians experienced difficulty in accessing prescription medication during the widespread health service disruption and rapid socio-economic transition of the 1990s. This paper examines trends and determinants of access in Russia during this period.
Methods Data were from nine rounds (1994–2004) of the Russia Longitudinal Monitoring Survey, a 38-centre household panel survey. Trends were measured in failing to access prescribed medication for the following reasons: unobtainable from a pharmacy, unable to afford and ‘other’ reasons. Determinants of unaffordability were studied in 1994, 1998 and 2004, using cross-sectional, age-adjusted logistic regression, with further multivariate analyses of unaffordability and failure to access for ‘other’ reasons in 2004.
Results After 1994, reporting of unavailability in pharmacies fell sharply from 25% to 4%. Meanwhile, unaffordability increased to 20% in 1998 but declined to 9% by 2004. In 1994, significant determinants of unaffordability were unemployment and lacking health care insurance in men. By 2004, determinants included low income and material goods in both sexes; rented accommodation and low education in men; and chronic disease and disability-related retirement in women. Not obtaining medicines for ‘other’ reasons was more likely amongst frequent male drinkers, and low educated or cohabiting women. Regional and gender differences were widest in 1998, coinciding with the Russian financial crisis.
Conclusions Rapid improvements in drug availability in the late 1990s in Russia are a probable consequence of a more liberalized pharmaceutical sector and an improved pharmacy network, whilst later improvements in affordability may relate to expanded health care insurance coverage and economic recovery after the 1998 crash. A significant minority still finds prescription costs problematic, notably poorer and sick individuals, with inequalities apparently widening. Non-monetary determinants of affordability indicate its partly subjective nature, however. Ongoing research into access is needed, due to recent national changes in prescription drug subsidies, and into doctor- and patient-related influences on access and prescribing for individual conditions.
Keywords: Russia, access to medication, drugs, health care, transition
KEY MESSAGES.
Many people experienced difficulties in obtaining prescription medication during Russia’s transition to a market economy.
These data demonstrate rapid improvements in the availability of prescription drugs during the late 1990s, as pharmacy networks and the pharmaceutical sector became more liberalized. During this period, however, drugs became less affordable, although this subsequently reversed with health care insurance expansion and broader economic improvements.
Better overall access to prescription medication masks inequalities, however, whereby the financially disadvantaged and the sick experience particular difficulties in affording prescribed drugs. Such inequalities appear to be increasing.
Background
The demise of the Soviet Union in 1991 led to a collapse in the state health service, with its universal coverage and medicines and medical supplies formally free at the point of use. In 1993, the Russian Federation introduced a compulsory health insurance scheme to replace the former tax-based financing model. However, inconsistent implementation between different regions, gaps in municipal funding for health care which was intended to cover those not able to pay premiums and the influx of illegal migrants after the dissolution of the USSR contributed to incomplete coverage (Burger et al. 1998; Twigg 1999). As a result, whilst the vast majority of the population was formally covered by 2000, disadvantaged individuals were less likely to be insured (Balabanova et al. 2003; Perlman et al. 2009).
Despite the existence of universal mandatory insurance, many people experienced difficulty in affording health care consultations in Russia, although less so than in other countries of the former Soviet Union (Balabanova et al. 2004). Thus, Russia’s health care system remained relatively protected from economic shocks and radical restructuring, compared with the health systems of Georgia and Armenia which have suffered extensive deterioration. And whilst inpatient drugs are free in public hospitals, outpatient and polyclinic drugs must be paid for by patients (Vienonen and Vohlonen 2001).
Whilst the number of prescription drugs available increased during the early 1990s, a survey as early as 1996 showed them to have become less affordable (Tragakes and Lessof 2003). In a 2001 survey in Russia, 16% of respondents reported ‘constantly’ going without drugs in the previous year due to unaffordability, with a further 32% doing so sometimes. The equivalent figures for seeing a health care worker were 11% and 27%, respectively. Part of the reason may be the widespread use of informal payments for health care, including drugs, in former communist countries (Lewis 2002; Balabanova et al. 2004). Typically, patients bear the cost of outpatient prescriptions in Russia (Tragakes and Lessof 2003). In 1996, a Rosstat survey indicated that 95% of survey respondents paid out-of-pocket for medical drugs (representing the majority of spending on drugs in Russia). This is likely to have a major impact on family budgets, of which the poorest tenth of households typically spend 9% on drugs (Marquez 2009).
Vulnerable groups experienced particular difficulties in accessing medication during the early 1990s, notably pensioners (Rush and Welch 1996) and individuals with serious chronic disease, such as diabetes. Lack of medication was a significant cause of morbidity for people with diabetes in many former Soviet countries, including Ukraine (Telishevka et al. 2001) and Kyrgyzstan (Hopkinson et al. 2004). Whilst the effect in transitional Russia was somewhat less severe (Balabanova et al. 2004), access to prescribed medication was still a problem, initially due to lack of availability, but increasingly due to unaffordability (Tragakes and Lessof 2003; Perlman and McKee 2007).
This paper aims to explore trends in access to medication during the years of the transition, and the factors associated with difficulty in accessing such drugs. Whilst previous analyses have focused on the experience of people with diabetes (Perlman and McKee 2007), these analyses focus on a broader section of the population.
Methods
Data
We used data from nine study rounds (1994–2003) of the Russia Longitudinal Monitoring Survey (RLMS), a panel study of households and the individuals within them. Participants came from 38 population centres across the Russian Federation. St Petersburg and Moscow were selected automatically, and the remaining 36 districts, or primary sampling units (PSUs), were sampled by stratifying districts according to socio-economic criteria, and selecting from each stratum using a probability proportional to size (PPS). Within the selected PSUs, urban and rural secondary sampling units (SSUs), census enumeration districts and villages, respectively, were selected. From each SSU, 10 households were selected from housing lists developed by the investigators. The first dwelling was chosen randomly, and the remainder at regular intervals. Thus, the sampling procedure was designed to achieve a study population that was broadly representative of the national population, but also ensuring that the two principal cities, with their particular characteristics, were included.
The overall response rate in the first round of Phase 2 (1994) was 84%, although it was lower in Moscow and St Petersburg (67%). In subsequent rounds, newly recruited households replaced those that left. More details of the study methods may be found at http://www.cpc.unc.edu/rlms. Because this is a panel study, many respondents appeared in more than one round of these analyses; only respondents aged over 18 were included.
Measures
Access to medication and other health variables
Respondents were first asked ‘in the last 30 days did a physician or some other specialist… write you a prescription or advise you to take some kind of medicine?’ Those who replied in the affirmative were then asked ‘were there any medicines prescribed or recommended for you in the last 30 days that you were not able to find or buy?’ A summary variable was created, consisting of four possible outcomes: a) obtained medication successfully, b) unable to find the drugs in a pharmacy (inaccessibility), c) unable to afford prescribed medication (unaffordability), or d) did not obtain the drugs for some other reason (this last outcome was summarized from three subcategories: did not have time to buy; did not want to buy; or physically could not obtain and had no-one to help).
Respondents were asked whether they had been ill in the last 30 days, and if so whether they self-treated or went to a doctor. They were also asked ‘Do you have any kind of chronic illness?’ (2004 survey only). An affirmative response led to specific questions about the presence of heart, lung, liver, kidney, gastrointestinal, spinal, or other chronic disease. There was a separate question: ‘Has a physician ever told you that you had diabetes or increased sugar in the blood?’ Responses were collapsed into chronic heart or lung disease, other chronic disease or none.
Demographic and socio-economic
Age was divided into three age bands: 18–39 years, 40–59 years, and 60 years and over. The cut-off point at 60 was chosen, as people of this age typically get free prescriptions. Whilst there are gender differences in pensionable age (men retire at 60 and women at 55), this was the best compromise. Area of residence was defined in two ways: (i) urban and rural and (ii) region of residence, divided into: Moscow and St Petersburg; Central or Central Black Earth; North or North West; North Caucasian; Ural; Western Siberian; and Eastern Siberian and Far Eastern. Marital status was divided into married, cohabiting, single (never married), divorced (and not remarried or cohabiting) and widowed. Number of household occupants was collapsed into living alone or not.
Education was divided into three categories: higher, complete secondary and incomplete secondary or primary. Adult equivalent household income was calculated by dividing household income by the square root of the number of household members (Atkinson et al. 1995). Quintiles were then calculated. An asset score was calculated by summing the number of selected consumer goods [colour television, video recorder, car, washing machine, dacha (country cottage/hut with land for growing food)]. Principal components analysis showed that these variables loaded onto a single factor, and could therefore be combined into a single continuous asset score. Employment status was collapsed into seven categories: employed, student (school or college), retired (not working), disabled (not working), maternity or childcare leave, unemployed or other. Respondents were asked if they owned or rented their home, or if they lived in a state-funded hostel. Household size was collapsed into respondents who lived alone, and those who shared their home with at least one other household member.
Health behaviours
Frequency of alcohol consumption was collapsed into four categories: one to three times a month, one to three times a week, four or more times a week, and none in the last month. Respondents were asked about current smoking: ‘Do you now smoke?’
Analyses
Trends in the proportion of respondents who were prescribed medication were calculated for each survey round between 1994 and 2004, by 3-year age band. Afterwards trends over the same period in the proportion unable to access the medication they were prescribed were calculated.
Cross-sectional multinomial logistic regression analyses were then undertaken on respondents present in the 2004 round. Using a baseline of those who could access medication, the determinants of two categories of not obtaining medication were studied: (a) unable to afford the prescription and (b) not obtaining for some ‘other’ reason. Two models were used, separately by gender: (i) variable of interest and age (continuous) only; and (ii) multivariate—variable of interest, education, number of household consumer goods, urban/rural area and age. Finally, further cross-sectional age-adjusted regression analyses were used to compare changes in the determinants of inability to afford prescription medication over time, using data from the 1994, 1998 and 2004 rounds.
Results
The overall proportion of respondents who obtained a prescription during the study remained fairly consistent, after a fall between the earliest two rounds (1994 and 1995) in the oldest groups (Table 1).
Table 1.
Proportion of respondents receiving a prescription, with 95% confidence interval, by gender and age band (RLMS 1994–2004)
| 1994 | 1995 | 1996 | 1998 | 2000 | 2001 | 2002 | 2003 | 2004 | |
|---|---|---|---|---|---|---|---|---|---|
| Males | |||||||||
| 18–39 years | 13.4 (11.8–15.0) | 7.7 (6.3–9.1) | 8.6 (7.1–10.1) | 8.6 (7.1–10.1) | 9.2 (7.7–10.7) | 9.3 (7.9–10.7) | 9.1 (7.7–10.5) | 9.6 (8.1–11.2) | 7.8 (6.4–9.1) |
| 40–59 years | 15.3 (13.4–17.2) | 14.2 (12.1–16.3) | 12.9 (10.9–15) | 12.9 (10.9–15) | 13.9 (11.6–16.3) | 15.9 (13.5–18.3) | 14.8 (12.6–17.0) | 14.2 (12.1–16.3) | 13.8 (11.8–15.8) |
| 60+ years | 30.8 (27.3–34.2) | 22.2 (18.7–25.7) | 20.9 (17.7–24.1) | 20.9 (17.7–24.1) | 19.7 (16.6–22.7) | 21.6 (18.6–24.6) | 23.7 (20.3–27.0) | 19.3 (16.0–22.5) | 21.2 (17.4–25.0) |
| Females | |||||||||
| 18–39 years | 18.6 (16.9–20.4) | 14.9 (13.1–16.7) | 14.9 (13.1–16.7) | 13.7 (11.9–15.4) | 14.2 (12.4–16.0) | 15.8 (14.0–17.6) | 14.1 (12.4–15.8) | 13.4 (11.8–15) | 13.3 (11.7–14.9) |
| 40–59 years | 23.7 (21.6–25.8) | 18.1 (16.0–20.2) | 20.2 (18.1–22.4) | 20.4 (18.1–22.7) | 18.0 (15.7–20.4) | 21.0 (18.7–23.3) | 20.7 (18.5–22.9) | 19.6 (17.5–21.7) | 17.9 (16.0–19.9) |
| 60+ years | 37.2 (34.6–39.8) | 29.5 (26.7–32.2) | 30.4 (27.6–33.1) | 29.5 (27.0–32.0) | 28.4 (25.9–30.8) | 32.1 (29.7–34.5) | 30.7 (28.3–33.1) | 31.7 (29.1–34.4) | 34.3 (31.4–37.2) |
Changes in obtaining the medication prescribed were far more pronounced, however. Between 1994 and 2000, the proportion of respondents who were prescribed medication but were unable to obtain drugs in a pharmacy declined sharply from 28% to only 4%. In contrast, the percentage unable to afford prescribed medication rose steadily from 8% in 1994 to 20% in 1998, followed by a steady decline to 9% in 2004. Between 4 and 5% of respondents did not obtain prescribed medication for ‘other’ reasons throughout the study (Figure 1). Relevant characteristics of respondents are shown in Table 2.
Figure 1.
Proportion of respondents unable to access prescribed medication by year (RLMS 1994–2004)
Table 2.
Distribution of variables amongst respondents aged 18 and over who received a prescription in 1994, 1998 and 2004 (RLMS)
| 1994 |
1998 |
2004 |
||||
|---|---|---|---|---|---|---|
| Male | Female | Male | Female | Male | Female | |
| No. (%) respondents | No. (%) respondents | No. (%) respondents | ||||
| Total | 657 (34.7) | 1,236 (65.3) | 443 (32.1) | 938 (67.9) | 511 (29.9) | 1198 (70.1) |
| Age | ||||||
| 18–39 years | 234 (35.6) | 363 (29.4) | 127 (28.7) | 230 (24.5) | 151 (29.6) | 303 (25.3) |
| 40–59 years | 211 (32.1) | 377 (30.5) | 150 (33.9) | 285 (30.4) | 188 (36.8) | 343 (28.6) |
| 60+ years | 212 (32.3) | 496 (40.1) | 166 (37.47) | 423 (45.1) | 172 (33.7) | 552 (46.1) |
| Accessing medicines | ||||||
| Able to access | 657 (100) | 1,236 (100) | 305 (69.16) | 602 (64.66) | 419 (82.2) | 990 (83.3) |
| Unable – not in pharmacy | 374 (57.1) | 720 (58.9) | 43 (9.8) | 91 (9.8) | 16 (3.1) | 37 (3.1) |
| Unable – no money | 197 (30.1) | 334 (27.3) | 73 (16.6) | 204 (21.9) | 51 (10.0) | 127 (10.7) |
| Unable – other reason | 44 (6.7) | 116 (9.5) | 20 (4.5) | 34 (3.7) | 24 (4.7) | 34 (2.9) |
| Education | ||||||
| Higher | 226 (34.5) | 502 (40.8) | 351 (37.5) | 507 (36.8) | 199 (38.9) | 539 (45.1) |
| Complete secondary | 182 (27.8) | 249 (20.2) | 130 (29.5) | 232 (24.8) | 158 (30.9) | 276 (23.1) |
| Incomplete secondary or primary | 247 (37.7) | 479 (38.9) | 155 (35.2) | 354 (37.8) | 154 (30.1) | 379 (31.7) |
| Marital status | ||||||
| Married | – | – | 330 (74.7) | 459 (49.0) | 333 (65.2) | 492 (41.1) |
| Never married | Omitted | Omitted | 30 (6.8) | 70 (7.5) | 58 (11.4) | 103 (8.6) |
| Divorced, not remarried | Coding | Coding | 28 (6.3) | 54 (5.8) | 32 (6.3) | 106 (8.9) |
| Widowed | Difference | Difference | 26 (5.9) | 84 (9.0) | 29 (5.7) | 390 (32.6) |
| Cohabiting, not married | – | – | 28 (6.3) | 270 (28.8) | 59 (11.6) | 107 (8.9) |
| Currently smoking | ||||||
| Yes | 343 (52.2) | 91 (7.4) | 219 (49.4) | 88 (9.4) | 299 (58.5) | 146 (12.2) |
| No | 314 (47.8) | 1,143 (92.6) | 224 (50.6) | 848 (90.6) | 212 (41.5) | 1052 (87.8) |
| Alcohol frequency | ||||||
| 1–3 times a month | 228 (34.7) | 381 (30.9) | 152 (34.3) | 284 (30.3) | 132 (25.9) | 336 (28.1) |
| 1–3 times a week | 152 (23.1) | 70 (5.7) | 102 (23.0) | 52 (5.6) | 134 (26.3) | 104 (8.7) |
| ≥4 times a week | 34 (5.2) | 10 (0.8) | 20 (4.5) | 5 (0.5) | 25 (4.9) | 13 (1.1) |
| None | 243 (37.0) | 773 (62.6) | 169 (38.2) | 596 (63.6) | 219 (42.9) | 742 (62.1) |
| Type of area | ||||||
| Urban | 504 (78.8) | 911 (77.1) | 325 (77.2) | 663 (74.8) | 391 (78.7) | 828 (74.0) |
| Rural | 136 (21.3) | 271 (22.9) | 96 (22.8) | 224 (25.3) | 106 (21.3) | 291 (26.0) |
| Chronic disease | ||||||
| None | Omitted | Omitted | Omitted | Omitted | 113 (22.1) | 187 (15.6) |
| Heart/lung disease | coding | coding | coding | coding | 207 (40.5) | 538 (44.9) |
| Other chronic disease (kidney/gastrointestinal/spine/other) | difference | difference | difference | difference | 191 (37.4) | 473 (39.5) |
| Lives alone | ||||||
| Yes | 30 (4.8) | 170 (14.5) | 31 (7.4) | 170 (19.2) | 32 (6.4) | 260 (23.2) |
| No | 601 (95.3) | 999 (85.5) | 390 (92.6) | 717 (80.8) | 465 (93.6) | 859 (76.8) |
| Region | ||||||
| Metropolitan (Moscow, St Petersburg) | 78 (11.9) | 142 (11.5) | 36 (8.1) | 94 (10.0) | 79 (15.5) | 200 (16.7) |
| Central, Central Black Earth | 153 (23.3) | 266 (21.5) | 93 (21.0) | 225 (24.0) | 86 (16.8) | 195 (16.3) |
| North, North West | 52 (7.9) | 83 (6.7) | 37 (8.4) | 78 (8.3) | 31 (6.1) | 79 (6.6) |
| Volga Vaytski, Volga Basin | 109 (16.6) | 184 (14.9) | 90 (20.3) | 136 (14.5) | 87 (17.0) | 183 (15.3) |
| North Caucasian | 72 (11.0) | 150 (12.1) | 53 (12.0) | 107 (11.4) | 71 (13.9) | 128 (10.7) |
| Ural | 93 (14.2) | 170 (13.8) | 53 (12.0) | 108 (11.5) | 77 (15.1) | 160 (13.4) |
| Western Siberian | 49 (7.5) | 139 (11.3) | 46 (10.4) | 97 (10.3) | 46 (9.0) | 129 (10.8) |
| Eastern Siberian and Far Eastern | 51 (7.8) | 102 (8.3) | 35 (7.9) | 93 (9.9) | 34 (6.7) | 124 (10.4) |
| Employment category | ||||||
| Employed | 347 (53.1) | 494 (40.0) | 206 (46.5) | 336 (35.9) | 227 (44.4) | 425 (35.5) |
| Student – school or higher education | 7 (1.1) | 23 (1.9) | 11 (2.5) | 22 (2.4) | 11 (2.2) | 26 (2.2) |
| Retired, not working | 57 (8.7) | 40 (3.2) | 34 (7.7) | 37 (4.0) | 51 (10.0) | 36 (3.0) |
| Disabled, not working | 538 (43.5) | 162 (36.6) | 431 (46.0) | 173 (33.9) | 578 (48.3) | 200 (30.6) |
| Maternity/childcare care | 71 (5.7) | 0 (0.0) | 54 (5.8) | 0 (0.0) | 69 (5.8) | 2 (0.3) |
| Unemployed | 52 (4.2) | 23 (5.2) | 40 (4.3) | 39 (7.6) | 42 (3.5) | 28 (4.3) |
| Other | 18 (1.5) | 7 (1.6) | 17 (1.8) | 10 (2.0) | 22 (1.8) | 12 (1.8) |
| Compulsory medical insurance | ||||||
| Yes | 423 (45.6) | 370 (76.6) | 790 (79.2) | 485 (94.9) | 1175 (98.1) | 233 (46.6) |
| No | 504 (54.4) | 113 (23.4) | 207 (20.8) | 26 (5.1) | 23 (1.9) | 267 (53.4) |
| Illness treated by health worker or self | ||||||
| Health worker | 903 (80.7) | 377 (88.1) | 739 (83.5) | 373 (80.4) | 924 (81.8) | 468 (82.8) |
| Self | 216 (19.3) | 51 (11.9) | 146 (16.5) | 91 (19.6) | 206 (18.2) | 97 (17.2) |
| Mean (SD) No. of obs | ||||||
| No. household consumer goodsa (0–5) | 2.2 (1.3) 657 | 1.9 (1.3) 1236 | 2.5 (1.4) 443 | 2.2 (1.4) 938 | 2.8 (1.3) 511 | 2.4 (1.4) 1198 |
| Adult equivalent household incomeb (Roubles) | 4578 (3770) 631 | 4793 (4783) 1168 | 2746 (2397) 421 | 2739 (2795) 887 | 5384 (4432) 497 | 5195 (7046) 1119 |
aHousehold consumer goods: colour TV, VCR, car, washing machine, dacha.
bAdult equivalent household income = household income/square root of number of occupants.
In 2004, cross-sectional regression analyses (Table 3) showed that lower educated men were more likely to be unable to afford medication, although socio-economic variables partly explained this association. As expected, low household income and household asset score were also associated with inability to afford drugs in both sexes. Women with chronic disease were 2–3 times as likely to state that they were unable to afford medication, even in the model that included education and material variables. Men and women in the North Caucasus were significantly more likely to be unable to afford medication than men in Moscow and St Petersburg. Men in rented accommodation were more likely to be unable to afford medication than those who owned their accommodation.
Table 3.
Determinants of being unable to access medication, both genders, 2004 (RLMS) logistic regression (able to access: males, n = 483; females, n = 1073)
| Unable to afford – male (n = 54) |
Unable to afford – female (n = 129) |
Other reason – male (n = 27) |
Other reason – female (n = 43) |
|||||
|---|---|---|---|---|---|---|---|---|
| Age adjusted | Multivariatea | Age adjusted | Multivariatea | Age adjusted | Multivariatea | Age adjusted | Multivariatea | |
| Age | ||||||||
| 18–39 years | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 40–59 years | 3.19 (0.90–11.28) | 3.86 (0.92–16.15) | 0.94 (0.41–2.14) | 1.01 (0.41–2.50) | 1.14 (0.20–6.53) | 1.31 (0.22–7.91) | 0.62 (0.13–2.97) | 0.60 (0.10–3.57) |
| 60+ years | 6.84 (0.76–61.34) | 6.87 (0.60–78.88) | 1.00 (0.26–3.81) | 1.01 (0.24–4.20) | 2.27 (0.10–53.73) | 2.49 (0.09–69.09) | 1.49 (0.12–18.40) | 0.79 (0.04–14.32) |
| Education | ||||||||
| Higher | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Complete secondary | 2.14 (0.98–4.67) | 1.80 (0.79–4.12) | 1.46 (0.90–2.37) | 1.26 (0.75–2.10) | 1.01 (0.38–2.71) | 1.10 (0.40–3.03) | 1.60 (0.65–3.93) | 2.12 (0.78–5.76) |
| Incomplete secondary or less | 2.05 (1.00–4.22) | 1.76 (0.80–3.84) | 1.43 (0.93–2.20) | 1.41 (0.88–2.27) | 0.84 (0.31–2.27) | 0.90 (0.31–2.63) | 3.23 (1.52–6.90) | 3.40 (1.31–8.80) |
| No. hh consumer goods (0–5, per good)b | 0.67 (0.54–0.83) | 0.65 (0.50–0.85) | 0.85 (0.74–0.98) | 0.82 (0.69–0.98) | 1.34 (0.96–1.88) | 1.31 (0.89–1.94) | 0.79 (0.64–0.99) | 0.91 (0.68–1.21) |
| Alcohol consumption | ||||||||
| 1–3 times a month | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 1–3 times a week | 1.30 (0.59–2.87) | 0.92 (0.39–2.14) | 0.73 (0.31–1.71) | 0.75 (0.31–1.79) | 3.01 (0.79–11.45) | 3.25 (0.84–12.59) | 1.36 (0.49–3.73) | 1.48 (0.50–4.37) |
| ≥4 times a week | 0.47 (0.06–3.79) | 0.34 (0.04–2.89) | 0.79 (0.10–6.30) | 0.96 (0.12–7.98) | 7.30 (1.51–35.25) | 7.96 (1.57–40.30) | – | – |
| None | 0.91 (0.45–1.86) | 0.62 (0.29–1.35) | 1.09 (0.70–1.70) | 1.02 (0.64–1.63) | 1.67 (0.45–6.13) | 1.55 (0.40–5.95) | 1.06 (0.52–2.16) | 0.80 (0.35–1.83) |
| Current smoking (vs not) | 2.04 (1.09–3.80) | 1.45 (0.75–2.82) | 1.52 (0.85–2.70) | 1.33 (0.71–2.49) | 1.30 (0.58–2.91) | 1.55 (0.63–3.80) | 1.90 (0.89–4.06) | 1.76 (0.72–4.30) |
| Marital status | ||||||||
| Married | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Single | 1.02 (0.35–2.91) | 0.78 (0.26–2.34) | 0.92 (0.46–1.87) | 0.76 (0.35–1.63) | 0.76 (0.23–2.54) | 0.92 (0.26–3.28) | 2.10 (0.76–5.80) | 1.89 (0.53–6.68) |
| Divorced | 1.45 (0.47–4.49) | 0.80 (0.24–2.70) | 1.83 (0.97–3.47) | 1.66 (0.85–3.27) | – | – | 1.96 (0.51–7.46) | 2.75 (0.63–11.97) |
| Widowed | 0.28 (0.03–2.24) | 0.26 (0.03–2.12) | 1.35 (0.81–2.28) | 1.19 (0.69–2.06) | 3.23 (0.58–18.13) | 4.17 (0.70–24.88) | 2.96 (1.02–8.56) | 2.70 (0.76–9.62) |
| Cohabiting | 1.88 (0.82–4.28) | 1.30 (0.53–3.20) | 1.38 (0.67–2.83) | 1.26 (0.58–2.72) | 0.68 (0.15–3.15) | 0.71 (0.15–3.38) | 3.00 (1.06–8.49) | 3.48 (1.03–11.70) |
| Area type | ||||||||
| Urban | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Rural | 1.34 (0.70–2.58) | 0.91 (0.45–1.83) | 0.93 (0.60–1.45) | 0.83 (0.53–1.32) | 0.97 (0.38–2.48) | 1.20 (0.44–3.27) | 0.67 (0.29–1.55) | 0.60 (0.25–1.45) |
| Adult equiv. hh income quintile (low–high)c | 0.69 (0.55–0.87) | 0.71 (0.55–0.90) | 0.74 (0.64–0.87) | 0.76 (0.65–0.89) | 1.20 (0.90–1.59) | 1.18 (0.88–1.59) | 0.92 (0.72–1.17) | 1.04 (0.80–1.34) |
| Chronic disease | ||||||||
| None | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Chronic heart/lung disease | 1.48 (0.64–3.42) | 1.63 (0.66–4.02) | 2.61 (1.25–5.46) | 3.33 (1.49–7.47) | 0.32 (0.10–1.02) | 0.34 (0.11–1.09) | 1.38 (0.51–3.74) | 1.42 (0.47–4.27) |
| Other chronic disease | 1.35 (0.60–3.03) | 1.33 (0.57–3.14) | 2.41 (1.19–4.89) | 2.89 (1.32–6.31) | 0.53 (0.22–1.31) | 0.60 (0.24–1.50) | 1.56 (0.67–3.64) | 1.45 (0.57–3.69) |
| Accommodation | ||||||||
| Owned | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Rented | 4.39 (1.69–11.43) | 4.20 (1.44–12.27) | 1.57 (0.68–3.60) | 1.66 (0.71–3.89) | – | – | 0.88 (0.20–3.81) | 1.28 (0.28–5.83) |
| Dormitory | 1.75 (0.37–8.16) | 1.51 (0.32–7.27) | 2.27 (0.83–6.20) | 2.12 (0.76–5.88) | 0.88 (0.11–7.00) | 0.84 (0.10–6.94) | 1.58 (0.35–7.08) | 1.45 (0.31–6.65) |
| Lives alone | 0.62 (0.14–2.70) | 0.55 (0.09–3.27) | 1.33 (0.84–2.11) | 1.19 (0.71–1.99) | 1.56 (0.34–7.04) | 2.26 (0.33–15.72) | 1.58 (0.61–4.07) | 1.11 (0.40–3.08) |
| Self (vs Dr) treated minor illness | 0.90 (0.42–1.93) | 1.03 (0.47–2.27) | 0.98 (0.60–1.60) | 1.07 (0.65–1.75) | 2.18 (0.85–5.54) | 2.27 (0.88–5.89) | 0.88 (0.38–2.02) | 0.99 (0.41–2.37) |
| No compulsory medical insurance | 0.92 (0.21–4.09) | 0.94 (0.20–4.38) | 1.54 (0.44–5.33) | 0.62 (0.08–4.73) | 1.41 (0.31–6.39) | 1.67 (0.35–7.99) | 5.29 (1.69–16.52) | 5.69 (1.44–22.46) |
| Area of residence | ||||||||
| Metropolitan (Moscow, St Petersburg) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Central, Central Black Earth | 1.49 (0.34–6.45) | 1.81 (0.33–9.91) | 0.77 (0.39–1.52) | 0.87 (0.43–1.77) | 0.56 (0.13–2.43) | 0.57 (0.12–2.59) | 0.49 (0.16–1.49) | 0.44 (0.12–1.59) |
| North, North West | 3.93 (0.82–18.83) | 5.72 (0.96–33.99) | 0.79 (0.31–2.06) | 0.87 (0.30–2.52) | 1.36 (0.30–6.13) | 1.25 (0.26–5.96) | 0.99 (0.29–3.36) | 1.35 (0.35–5.18) |
| Volga Vaytski, Volga Basin | 2.64 (0.69–10.14) | 3.31 (0.68–16.11) | 0.90 (0.46–1.76) | 1.01 (0.50–2.04) | 0.65 (0.17–2.53) | 0.65 (0.16–2.60) | 0.60 (0.21–1.73) | 0.65 (0.20–2.08) |
| North Caucasian | 8.65 (2.42–30.95) | 12.06 (2.55–57.06) | 2.21 (1.18–4.14) | 2.78 (1.34–5.75) | 0.79 (0.18–3.44) | 0.77 (0.16–3.64) | 0.66 (0.20–2.21) | 0.75 (0.19–3.04) |
| Ural | 2.18 (0.52–9.10) | 2.47 (0.45–13.44) | 1.40 (0.74–2.66) | 1.39 (0.70–2.78) | 1.11 (0.32–3.83) | 1.13 (0.32–4.05) | 0.23 (0.05–1.07) | 0.24 (0.05–1.21) |
| Western Siberian | 3.02 (0.69–13.29) | 3.91 (0.70–21.81) | 0.77 (0.36–1.67) | 0.93 (0.41–2.09) | 0.36 (0.04–3.22) | 0.31 (0.03–2.90) | 0.71 (0.23–2.19) | 0.65 (0.17–2.41) |
| Eastern Siberian and Far Eastern | 3.24 (0.68–15.34) | 3.57 (0.60–21.26) | 0.59 (0.24–1.44) | 0.67 (0.26–1.70) | 0.86 (0.16–4.70) | 0.91 (0.15–5.41) | 1.22 (0.45–3.29) | 1.00 (0.30–3.35) |
| Employment status | ||||||||
| Employed | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Student – school or higher education | 0.44 (0.11–1.75) | 0.39 (0.08–1.97) | 0.25 (0.07–0.89) | 0.17 (0.04–0.68) | 0.57 (0.14–2.27) | 0.32 (0.07–1.61) | 3.05 (0.95–9.82) | 1.81 (0.37–8.77) |
| Retired, not working | 1.24 (0.44–3.52) | 1.13 (0.38–3.38) | 2.07 (0.75–5.76) | 2.11 (0.74–6.04) | 0.97 (0.27–3.51) | 0.96 (0.25–3.71) | – | – |
| Disabled, not working | 1.26 (0.49–3.23) | 0.94 (0.33–2.67) | 2.26 (1.14–4.48) | 2.32 (1.11–4.83) | 0.23 (0.04–1.31) | 0.14 (0.02–0.97) | 0.84 (0.25–2.89) | 0.34 (0.08–1.47) |
| Maternity/childcare leave | – | – | 1.21 (0.50–2.91) | 0.94 (0.34–2.63) | – | – | 2.50 (0.81–7.72) | 1.50 (0.37–6.14) |
| Unemployed | 2.18 (0.85–5.63) | 2.10 (0.76–5.76) | 0.78 (0.23–2.68) | 0.81 (0.23–2.82) | 0.39 (0.05–3.02) | 0.31 (0.04–2.62) | – | – |
aMultivariate model: education, no household consumer goods, urban/rural area, age.
bHousehold consumer goods: colour TV, VCR, car, washing machine, dacha.
cAdult equivalent household income = household income/square root of number of occupants.
Men who drank four or more times a week were seven times as likely to report not obtaining prescribed drugs for ‘other’ reasons than men who drank 1–3 times a month. In women, not obtaining medication for ‘other’ reasons was significantly associated with low education, not having compulsory medical insurance and asset score and marital status, although the latter two associations were partly explained by socio-economic factors.
Comparisons of the determinants of being unable to afford prescription medication in 1994, 1998 and 2004, respectively, are shown in Table 4, and aim to demonstrate the changes in factors associated with unaffordability during different stages of the transition. In men, the role of socio-economic factors as determinants of access to drugs increased during the transition. In 1994, men who lacked compulsory medical insurance, had low household income or who were unemployed reported unaffordability more often. Over time, a wider range of socio-economic characteristics were seen to be associated with unaffordability of drugs, with the effect of low education, smoking, progressively fewer material assets and living in rented accommodation emerging as important in the more recent 2004 survey round. Adult equivalent household income quintile also influenced affordability in both sexes in 2004. Among women, there were strong regional differences in affordability, but these were less marked in men. This was particularly so in 1998 when female residents of five regions reported lower affordability than women in the main cities, although the effect in the easternmost part of Russia was significant in each time period. In 1998, women were significantly more likely to report inability to afford medication than men, although inequalities are mainly regional rather than socio-economic.
Table 4.
Determinants of being unable to afford medication (baseline: being able to access medication) 1994, 1998 and 2004 (RLMS)
| Males |
Females |
|||||
|---|---|---|---|---|---|---|
| 1994 | 1998 | 2004 | 1994 | 1998 | 2004 | |
| Cannot afford (n) | 44 | 73 | 54 | 116 | 206 | 129 |
| Able to obtain (n) | 378 | 314 | 483 | 734 | 611 | 1073 |
| Gender (f vs m) | 1.34 (0.92–1.93) | 1.44 (1.06–1.94) | 1.01 (0.72–1.42) | |||
| Age | ||||||
| 18–39 years | 1 | 1 | 1 | 1 | 1 | 1 |
| 40–59 years | 1.48 (0.76–2.86) | 1.03 (0.27–3.93) | 3.19 (0.90–11.28) | 0.68 (0.40–1.13) | 1.21 (0.49–2.98) | 0.94 (0.41–2.14) |
| 60+ years | 0.71 (0.23–2.19) | 3.33 (0.35–31.80) | 6.84 (0.76–61.34) | 0.94 (0.41–2.14) | 3.32 (0.75–14.57) | 1.00 (0.26–3.81) |
| Education | ||||||
| Higher | 1 | 1 | 1 | 1 | 1 | 1 |
| Complete secondary | 0.98 (0.62–1.54) | 0.79 (0.34–1.80) | 2.14 (0.98–4.67) | 0.72 (0.50–1.05) | 0.73 (0.40–1.31) | 1.46 (0.90–2.37) |
| Incomplete secondary or less | 0.96 (0.63–1.44) | 0.62 (0.29–1.36) | 2.05 (1.00–4.22) | 1.41 (1.01–1.97) | 0.95 (0.55–1.65) | 1.43 (0.93–2.20) |
| No. household consumer goods (0–5, ordinal, per good)a | 0.92 (0.80–1.06) | 0.93 (0.74–1.18) | 0.67 (0.54–0.83) | 1.10 (0.99–1.22) | 1.02 (0.87–1.19) | 0.85 (0.74–0.98) |
| Alcohol consumption | ||||||
| 1–3 times a month | 1 | 1 | 1 | 1 | 1 | 1 |
| 1–3 times a week | 0.71 (0.44–1.16) | 0.93 (0.30–2.92) | 1.30 (0.59–2.87) | 0.91 (0.49–1.66) | 1.98 (0.50–7.74) | 0.73 (0.31–1.71) |
| ≥4 times a week | 0.70 (0.30–1.66) | 1.67 (0.83–3.35) | 0.47 (0.06–3.79) | 0.84 (0.16–4.45) | 0.82 (0.50–1.35) | 0.79 (0.10–6.30) |
| None | 1.32 (0.88–1.98) | 1.16 (0.44–3.07) | 0.91 (0.45–1.86) | 1.00 (0.73–1.36) | 1.30 (0.57–2.94) | 1.09 (0.70–1.70) |
| Current smoking (vs not) | 1.14 (0.78–1.65) | 0.47 (0.23–0.99) | 2.04 (1.09–3.80) | 1.28 (0.77–2.13) | 1.93 (0.98–3.82) | 1.52 (0.85–2.70) |
| Marital status | ||||||
| Married | 1 | 1 | 1 | 1 | ||
| Single | (omitted, | 1.48 (0.42–5.14) | 1.02 (0.35–2.91) | (omitted, | 0.89 (0.39–2.01) | 0.92 (0.46–1.87) |
| Divorced | coding | 0.44 (0.06–3.41) | 1.45 (0.47–4.49) | coding | 1.33 (0.55–3.21) | 1.83 (0.97–3.47) |
| Widowed | difference) | 1.00 (0.22–4.57) | 0.28 (0.03–2.24) | difference) | 0.69 (0.26–1.81) | 1.35 (0.81–2.28) |
| Cohabiting | 1.84 (0.60–5.69) | 1.88 (0.82–4.28) | 1.34 (0.73–2.44) | 1.38 (0.67–2.83) | ||
| Region | ||||||
| Urban | 1 | 1 | 1 | 1 | 1 | 1 |
| Rural | 0.68 (0.29–1.58) | 0.76 (0.39–1.48) | 1.34 (0.70–2.58) | 1.20 (0.75–1.92) | 0.96 (0.66–1.41) | 0.93 (0.60–1.45) |
| Adult equivalent household income quintile (low–high)b | 0.86 (0.75–0.99) | 0.81 (0.64–1.04) | 0.69 (0.55–0.87) | 0.94 (0.85–1.03) | 1.16 (0.98–1.36) | 0.74 (0.64–0.87) |
| Accommodation | ||||||
| Owned | 1 | 1 | 1 | 1 | 1 | 1 |
| Rented | 0.55 (0.07–4.34) | 1.80 (0.45–7.29) | 4.39 (1.69–11.43) | 1.80 (0.80–4.06) | 0.96 (0.35–2.67) | 1.57 (0.68–3.60) |
| Dormitory | – | 2.12 (0.61–7.37) | 1.75 (0.37–8.16) | 1.91 (0.52–6.99) | 1.05 (0.41–2.73) | 2.27 (0.83–6.20) |
| Lives alone (vs not) | 0.95 (0.21–4.24) | 2.10 (0.87–5.07) | 0.62 (0.14–2.70) | 1.26 (0.72–2.19) | 0.84 (0.54–1.30) | 1.33 (0.84–2.11) |
| Self (vs Dr) treated minor illness | 1.72 (0.79–3.75) | 1.33 (0.62–2.87) | 0.90 (0.42–1.93) | 1.36 (0.84–2.19) | 0.97 (0.62–1.53) | 0.98 (0.60–1.60) |
| No compulsory med insurance (vs insured) | 2.41 (1.07–5.41) | 1.01 (0.55–1.85) | 0.92 (0.21–4.09) | 1.68 (1.04–2.72) | 1.09 (0.74–1.61) | 1.54 (0.44–5.33) |
| Area of residence | ||||||
| Metropolitan (Moscow, St Petersburg) | 1 | 1 | 1 | 1 | 1 | 1 |
| Central, Central Black Earth | 0.98 (0.27–3.50) | 1.08 (0.35–3.34) | 1.49 (0.34–6.45) | 1.07 (0.53–2.1 4) | 1.86 (0.93–3.73) | 0.77 (0.39–1.52) |
| North, North West | – | 1.77 (0.50–6.22) | 3.93 (0.82–18.83) | 0.73 (0.25–2.17) | 1.83 (0.77–4.37) | 0.79 (0.31–2.06) |
| Volga Vaytski, Volga Basin | 3.88 (1.19–12.65) | 1.17 (0.38–3.58) | 2.64 (0.69–10.14) | 1.27 (0.60–2.68) | 1.45 (0.68–3.11) | 0.90 (0.46–1.76) |
| North Caucasian | 1.61 (0.41–6.40) | 0.81 (0.23–2.81) | 8.65 (2.42–30.95) | 1.06 (0.46–2.44) | 2.58 (1.20–5.53) | 2.21 (1.18–4.14) |
| Ural | 1.75 (0.48–6.33) | 1.71 (0.53–5.48) | 2.18 (0.52–9.10) | 0.50 (0.21–1.21) | 4.01 (1.90–8.47) | 1.40 (0.74–2.66) |
| Western Siberian | 0.80 (0.14–4.65) | 1.13 (0.32–4.00) | 3.02 (0.69–13.29) | 0.89 (0.37–2.12) | 3.39 (1.57–7.32) | 0.77 (0.36–1.67) |
| Eastern Siberian and Far Eastern | 2.39 (0.59–9.64) | 1.45 (0.40–5.18) | 3.24 (0.68–15.34) | 3.25 (1.50–7.07) | 3.18 (1.48–6.86) | 0.59 (0.24–1.44) |
| Employment status | ||||||
| Employed | 1 | 1 | 1 | 1 | 1 | 1 |
| Full-time student | 1.83 (0.19–17.76) | 0.74 (0.18–3.01) | 0.44 (0.11–1.75) | 0.36 (0.05–2.82) | 0.40 (0.11–1.43) | 0.25 (0.07–0.89) |
| Retired, not working | 1.14 (0.30–4.36) | 2.29 (0.97–5.40) | 1.24 (0.44–3.52) | 1.52 (0.41–5.67) | 1.36 (0.59–3.10) | 2.07 (0.75–5.76) |
| Disabled, not working | 1.36 (0.46–4.03) | 1.49 (0.62–3.55) | 1.26 (0.49–3.23) | 1.10 (0.56–2.18) | 1.41 (0.81–2.42) | 2.26 (1.14–4.48) |
| Maternity/childcare leave | (omitted) | (omitted) | (omitted) | 1.71 (0.74–3.97) | 1.00 (0.46–2.15) | 1.21 (0.50–2.91) |
| Unemployed | 4.60 (1.65–12.82) | 1.00 (0.31–3.25) | 2.18 (0.85–5.63) | 0.40 (0.09–1.71) | 1.42 (0.64–3.14) | 0.78 (0.23–2.68) |
aHousehold consumer goods: colour TV, VCR, car, washing machine, dacha.
bAdult equivalent household income = household income/square root of number of occupants.
Discussion
Summary of findings
During the study, difficulty in accessing medication declined sharply. However, unaffordability rose during the early 1990s, culminating predictably in the 1998 economic shock, although declining afterwards to its previous level. Low education in men, poor financial situation and chronic disease in women were all associated with lack of affordability; failure to access medication for other reasons was linked to low education and heavy alcohol consumption. Regional and gender differences were greatest in 1998, during the financial crisis. Whilst the strong effects of unemployment and lack of insurance in 1994 have since attenuated, income and material inequalities in affordability appear to have widened over time.
Limitations
A key limitation of this study is that no information was sought about the nature of the medication, or indeed the illness that led to the prescription. Many drug treatments in Russia are not based on the same standards of research evidence as in the West (Danishevski et al. 2007), so it is uncertain whether the prescribing was clinically appropriate, or how good the quality of the medication was. Nevertheless, the trends in affordability and access to medication were very marked, and since rapid changes in prescribing practice are unlikely, these trends can be assumed to be reliable. Equally, these findings exclude patients who would benefit from medication who either did not receive a prescription or did not consult a medical professional during the 30 days prior to the survey, including those who used herbal or other home-made remedies. Furthermore, we do not know the cost of the drugs that were prescribed, and whether the prescription was given in a hospital or a polyclinic, although since hospital prescriptions are often free, at least for inpatients, we can assume that many of them were from a polyclinic (Vienonen and Vohlonen 2001). In addition, different socio-economic groups may buy different combinations of drugs, with herbal medicines becoming increasingly popular amongst the less well off, and those disillusioned with conventional medicine (Curtis 1996; Ingram 1996).
Since some respondents participated over several rounds, it is important to consider the possible impact of attrition on the findings of this panel survey. Previous analyses showed that a quarter of individual respondents left RLMS over this period, who were more often younger, less educated, urban residents on higher incomes. In addition, individuals in poor health (who would also be more likely to need prescription medication) in RLMS were also more likely to die (Perlman 2006; Perlman and Bobak 2008). However, the relatively consistent prescription rate after the second round suggests that, with the possible exception of the changes in older people before the second round, a major effect of selective attrition is unlikely. In addition, the large improvements in accessibility, together with changes in affordability that parallel household income changes shown previously (Perlman 2006), suggest that selective losses would be unlikely to have a major effect on the results.
Discussion of findings
At the start of the survey, many respondents were simply unable to access medicines from pharmacies, consistent with the disrupted health care system and unavailability of medicines during the early years of the transition. However, the large-scale privatization of the pharmaceutical sector has led to improved distribution and availability of drugs throughout Russia and widening consumer choice since the early 1990s (Balabanova and Coker 2008), but unaffordability has increased. The particularly high proportion unable to afford medicines in 1998 coincided with the ‘rouble crisis’, a financial crash two months prior to the survey of that year. Households had to make difficult choices, balancing the costs of medication against other basic needs for food and clothing. Since 1998, there has been a considerable decline, but it is still problematic that by 2004, 10% of respondents stated they could not afford prescribed medication.
Affordability is subjective, however, and these data demonstrated the influence of non-monetary factors. The higher likelihood of unaffordability of medicines amongst lower educated men, independent of material variables, could perhaps indicate that this group made medication a lower priority or are less able to orient among the many available medications and discuss substitutes to the prescribed treatment. They may also be socially isolated and stigmatized. More educated people have been shown to use a more strategic and consumerist approach in order to get ‘better’ health care (in Russia often equated with using brand name medication), and attach more importance to keeping in good health (Rusinova and Brown 2003).
Some of the determinants of unaffordability of prescriptions changed over time. The stronger effect of low income and material goods in the most recent round indicates widening inequalities in health care. In contrast, the strong effect of unemployment in the earliest round disappeared subsequently, perhaps showing the effect of improved coverage by social security programmes. The association with lack of health insurance in 1994 also disappeared, perhaps indicating that as social security programmes became more widespread (Balabanova et al. 2003; Perlman et al. 2009), non-insurance no longer reflected poverty. The association in 2004 between being uninsured and not accessing medication for other reasons in women could suggest that both measures indicate a lack of concern over one’s health.
In 1998, the year of the financial crisis, income quintile had the weakest effect on affording medication, perhaps because incomes were generally low. This was the only year in which gender differences were significant, suggesting that women were worst affected, or perhaps prioritized medication the least. Large regional differences in that year may indicate an unequal impact of the financial crash across the country. Interestingly, the overall prescribing rate did not decline during the crisis, suggesting that people were seeking medical care as before. This is probably because the increase in population health care insurance coverage, known to influence medical care-seeking behaviour, enabled individuals to seek help despite their individual circumstances (Balabanova et al. 2003; Perlman et al. 2009). Surprisingly, the availability of drugs was not affected negatively by the macro-economic consequences of the financial crisis.
Amongst men, frequent drinkers were less likely to access medicines for ‘other’ reasons. Some of these men may have been alcohol dependent, which perhaps made medication less of a priority for them, or affected their financial decision making. A further possibility is that the widespread use of alcohol as an alternative to prescription medication in Russia may be more common amongst frequent drinkers.
The reasons for not obtaining prescribed medication from a pharmacy have not been extensively researched. However, one small study in the UK identified lack of affordability, as shown here, and two further reasons: poor patient understanding, including difficulties in the doctor–patient relationship; and a patient’s desire to control his treatment. Whilst we know relatively little about prescribing in the consultation in Russian medical care, implementation of Western-style general practice training programmes during the transition faced difficulties and resistance (Rese et al. 2005). In the Soviet period, most patients appeared to expect a prescription, and the entrance of international drug companies after 1991 put more pressure on doctors to prescribe (Vienonen and Vohlonen 2001). Throughout the region, there have been preferences for branded drugs, seen to be of higher quality than those domestically produced, as a consequence of increase in imports and direct advertising to the public and physicians (Mrazek and Fidler 2004). An autocratic style of medical practice (Danishevski et al. 2007), especially amongst older male physicians (Bernstein and Shuval 1994), and a lack of evidence-based treatments (Danishevski et al. 2007), with prescribing based more on availability and affordability than effectiveness (Tragakes and Lessof 2003), may simply lead to patients choosing not to use medicines that they do not trust as they were prescribed for reasons that they do not understand.
Little is known about the role of pharmacists in the former Soviet Union, but there are indications that their advice and attitudes may influence patterns of consumption; for example less educated men with alcohol problems may receive insufficient attention when seeking to obtain drugs (Mrazek and Fidler 2004). There is also a sizeable black market in Russia providing access to illegally imported drugs (Mrazek and Fidler 2004), which may be more affordable than those available through the official pharmacies. However, more detailed research is required into the dynamics of the doctor–patient relationship and health and health care beliefs in the Russian context, and its effect on prescribing.
At the same time, the problem has been recognized to some extent by the Russian government and there have been a range of measures on the supply-side to control pharmaceutical prices (Mossialos 1999). The shift from in-kind to monetary benefits affecting both pensioners and the disabled, who together form more than a quarter of the population and are disadvantaged, could in effect undermine the value of the benefits, especially if inflation is present, making prescribed drugs less affordable for this group (Wengle and Rasell 2008).
Thus the ‘Provision of Supplemental Medicines’, a state-subsidized federal programme of free prescriptions covering 14 million benefit claimants, was introduced in 2005, but funds were insufficient, partly because the budget was too small and partly due to loss of funds through corruption. By early 2007, people were unable to access these medications free of charge, and deaths occurred. Despite protests, including groups of pensioners burning effigies of the health minister, this situation has proved difficult to resolve, and the indications are that since these data were obtained, prescription medication remains unaffordable for many (Parfitt 2007). This paper demonstrates the magnitude of the problems, and also the need to introduce demand-side measures, given the very diverse pathways people use to obtain health services and medication.
Conclusions and policy implications
Affordability of medication continues to be a problem which Russian health care reform needs to address in order to improve the health of the population. As the burden of chronic disease in Russia increases, regular access to affordable medication, especially for those unable to obtain it, should be a policy priority. The ‘law of halves’, coined in the USA in the 1970s (Wilber and Barrow 1972), referring to hypertension, states that half the individuals who need treatment are not diagnosed, of those half are not prescribed appropriate medication, and of the latter, half are not taking it. Therefore further study into the factors that influence accessibility of prescription medication should form part of a wider body of research into prescribing practice in Russia and in the former Soviet Union.
Funding
FP’s work was supported by a Wellcome Trust Intermediate Clinical Fellowship [Grant number: 035610].
Acknowledgements
We thank the Russia Longitudinal Monitoring Survey Phase 2, funded by USAID and NIH (R01-HD38700), Higher School of Economics and Pension Fund of Russia, and the Carolina Population Center and Russian Institute of Sociology for making these data available.
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