What this is
- This study investigates unmet medical needs in Germany, focusing on delayed and forgone care.
- It examines the reasons behind these unmet needs, including waiting times, travel distance, and financial costs.
- The research highlights inequalities based on social factors such as sex, age, education, and income.
Essence
- Unmet medical needs are prevalent in Germany, with 30% of respondents reporting forgone care and 34% experiencing delays due to waiting times. Social inequalities significantly influence these unmet needs.
Key takeaways
- 30% of respondents reported at least one reason for forgone care, primarily due to waiting times (23%) and financial costs (11%).
- Female sex, younger age, lower education, and lower income are significantly associated with unmet medical needs, particularly forgone care.
- More than half of those with unmet needs reported a perceived worsening of their health, indicating a strong link between access barriers and health outcomes.
Caveats
- The study's sample may not fully represent the general population due to selection bias, as only internet users were included.
- Recall bias could affect the accuracy of reported delays and forgone care, as participants may not accurately remember their experiences.
Definitions
- unmet need: Differences between necessary health services and those actually received due to access barriers.
AI simplified
Introduction
Unmet need is defined as “differences between services judged necessary to deal appropriately with health problems and services actually received” [1]. In case of subjective unmet need, a person perceives need of health care but does not receive or use respective services due to access barriers beyond his or her control [2]. This study was based on the concept of subjective, not-chosen unmet need which is an established measure of health care access and its underlying barriers [2–4]. Different reasons for this kind of unmet need are distinguished. Most relevant from a health policy perspective and predominantly included in previous research are reasons referring to availability (waiting time, travel distance) and affordability (financial costs) which are also central components in the conceptual framework of health care access by Levesque et al. (2013) [2–7]. Apart from these main reasons, further individual rationales for unmet need are work and family commitments, fear of doctor and treatment, preferring to wait and see, and not knowing any good doctor [8]. To ascertain unmet need, measures of forgone and delayed care have been used in various surveys [5, 9–12].
Prevalence of unmet need varies across different countries. According to EHIS (European Health Interview Survey) data among 30 OECD and EU countries, on average, 28% of adults reported unmet need in a period of 12 months due to financial costs (16%), long waiting times (18%), or travel distance/transport problems (4%) [5]. Moreover, all types of unmet need were much more pronounced among the least wealthy in nearly all countries under study [5]. Even though levels of unmet need vary across surveys due to different methods and approaches [6, 12, 13], other international surveys showed similar patterns [4, 6, 14–16]. Apart from income, further social predictors are relevant for unmet need. Women, younger persons, people with limited insurance coverage, lower occupational position, and migration history were more likely to report forgone care [4, 14, 15, 17–19]. Educational inequalities were less pronounced [14, 15]. Although unmet need is an established measure in health services research [3, 5], empirical studies on negative health effects of subjective unmet need are less common. Two longitudinal studies from the U.S. showed associations between delayed or forgone care and adverse health outcomes [20, 21]. Similar associations were found in Europe and Asia [22–24]. Current data regarding delayed and forgone care due to the COVID-19 pandemic in the Netherlands reported negative health effects as a result of postponed care [25, 26].
The case of Germany
The German health care system is based on a social health insurance system and is primarily funded by insurance contributions. Health insurance is compulsory and characterized by a dual structure of statutory health insurance (SHI) and substitutive private health insurance (PHI). Citizens with an income over a certain limit, self-employed, and public servants can choose a PHI for substitutive full coverage. Around 11% of the population is covered through PHI. Privately insured people experience some benefits compared to those covered by SHI (e.g. shorter waiting times) [27]. German studies of unmet need differ in terms of populations under study and survey methods. Overall, 32% reported forgone or delayed care due to waiting time (25%), travel distance (4%), or financial costs (14%), with higher rates among lower income groups (EHIS wave 2 data). Income-related inequalities for unmet need were particularly high for Germany [5]. Further surveys including data for Germany (e.g. EU-SILC, SHARE) varied in the level of unmet need, but also showed various social inequalities [9, 28–32]. Data about differences due to insurance status are rare. Finally, health risks of subjective unmet need in Germany are largely unknown.
Against this background, the present study includes following additional contributions: First, current detailed data is provided. Unmet need among the German population is a prevailing issue due to current changes and shortages in health services (e.g. supply of outpatient care in rural or deprived areas) [33–36]. The present study provides first detailed analyses after the COVID-19 pandemic which is known to have a great impact on delayed and forgone health care utilization in various European countries and the U.S [37, 38]. Second, social regional deprivation was included as it was shown that residing in more deprived urban or rural districts was associated with lower general practitioner (GP)availability in Germany [34, 35]. Accordingly, comprehensive multilevel analyses were conducted. Third, delayed and forgone care were separately analysed for an improved assessment of barrier-related unmet need in the German population. Fourth, associations between health insurance and different reasons for unmet need were included. Fifth, self-perceived health risks due to unmet need among the general population were additionally ascertained. Thus, the study aimed to examine the magnitude of unmet need, its reasons, and health-related consequences. In multilevel analyses, individual and regional social determinants of unmet need and perceived health risk were additionally examined. Accordingly, the following research questions were addressed:
Methods
Study design and population
The questionnaire was initiated by the research team. Analyses were based on a cross-sectional online survey that was conducted by a social research institute (forsa) in winter 2022/23. An adult population sample (age ≥ 18 years) was randomly drawn from a panel. This panel comprises a sample of the population living in Germany which was recruited via telephone using a dual-frame approach that included landline as well as mobile phone numbers. It is regularly refreshed and currently consists of about 150,000 people. 5,619 German-speaking individuals were randomly selected from the panel and invited to participate in the present survey via email. After three reminders, N = 2,201 individuals participated. Based on power calculations (statistical power = 0.8; α = 0.05; Cohen’s d = 0.2), a sample size of about N = 2,200 was aimed at to identify statistically reliable differences between various subgroups under study (e.g. privately and statutorily insured). The sample was weighted for age, sex, federal state, and education (using the iterative proportional fitting approach [39]) according to the official statistics provided by the Federal Statistical Office of Germany [40]. Thus, the weighted sample adequately represents the adult population in Germany regarding these sociodemographic characteristics. All procedures were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The survey was approved by the Local Psychological Ethics Committee at the Center for Psychosocial Medicine, University Medical Center Hamburg (No. LPEK-0563).
Outcome variables
Unmet need was surveyed as follows: Initially, the participants were asked if there had been a need for examination or treatment. If not, participants were excluded from the analyses. Subsequently, two questions were asked regarding delayed care [41, 42]: (1) “Have you experienced delay in getting health care in the past 12 months because the time needed to obtain an appointment was too long?”, and (2) “Have you experienced delay in getting health care in the past 12 months due to distance or transportation problems?”. Respective response options were “yes”, “no”, “don’t know”/”not specified”. In terms of forgone care, three questions were asked including the most relevant, system-related reasons [43]: “During the past 12 months did it ever happen that you did not get the medical treatment you needed because you could not pay for it?”, (2) “During the past 12 months did it ever happen that you did not get the medical treatment you needed because the treatment you needed was not available where you live or nearby?”, and (3) “During the past 12 months did it ever happen that you did not get the medical treatment you needed because the waiting time/waiting list was too long?”. Again, response options were “yes”, “no”, “don’t know”/”not specified”. Responses of these three types of foregone care were combined to assess if at least one type of forgone care was experienced in the past 12 months. In case of reporting delayed or forgone care, respondents were requested to assess related health risks: “If so, has this made your symptoms worse?” (response options: “yes”, “no”, “don’t know”/”not specified”). To consider all three reasons of system-related unmet need, this question was asked in terms of delay due to waiting time, delay due to distance, and forgoing treatment due to costs.
Independent variables
The following individual social characteristics of the respondents were considered as predictors: age, sex, education, income, and migration history. Age was categorized into three age groups: 18–40 years, 41–59 years, and ≥ 60 years. Educational level was assessed according to the established CASMIN educational classification which is a hierarchically structured measurement of certificates including the general and vocational qualifications [44]. The nine original CASMIN-levels were merged into three educational groups: low (levels 1a, 1b and 1c), intermediate (2a and 2b), and high (2c_gen, 2c_voc, 3a and 3b). Monthly net household income was equalized to consider household size and composition and further divided into tertiles. In terms of migration history, respondents were classified into three groups: people who immigrated themselves (1st generation migrants); people who were born in Germany, but whose parents (one or both) had immigrated (2nd generation migrants), and those without a migration history. Furthermore, insurance status (statutory/private) was introduced.
As regional disparities were also shown to be an important determinant of health care access [33–35, 45] the German Index of Social Deprivation (GISD) was introduced on the area level [46]. This index uses administrative data of education (e.g. proportion of employees with university degree and without qualification), employment (e.g. unemployment rate, gross wage and salary), and income (net household income, debtor quota, tax revenue) at the district and municipality level. In the present analyses, classification was based on postal codes.
Analyses
As n = 246 (11.2%) of the participants indicated that there was no need for examination or treatment in the past 12 months, a remaining sample of N = 1,955 was included in the analyses. First, prevalence of unmet need (i.e. delayed and forgone care due to different reasons) and perceived health risk due to unmet need was calculated. Second, multilevel logistic regressions were carried out to consider potential predictors of unmet need on individual and area level. In fully adjusted models, associations between delayed and forgone care and all individual social characteristics (sex, age, migration history, education, income) and insurance status were calculated. The GISD was used as level 2 unit in the mixed model (random intercept) to account for differences in regional deprivation. Odds ratios (OR), 95%-confidence intervals (95%-CI), and p-values are documented. Variance, intraclass correlation coefficient (ICC), and the median odds ratio (MOR) are also reported to provide information about the contribution of the area level to the explained variance of the model. For a more intuitive interpretation of the area level variance, the MOR was introduced by Merlo et al. [47] and was calculated as a measure of the mean variation in unmet need between the different deprived groups [48]. The same procedures of multivariate analyses were conducted for perceived health risk due to delayed and forgone care as dependent variables. Due to a small number of cases, delay due to waiting time and delay due to distance were matched for these analyses. Analyses were carried out using the Statistical Package for the Social Sciences SPSS 29 [49].
Results
Detailed information about the sample characteristics are shown in Table 1.
Table 2 shows magnitudes of unmet need and perceived health risk due to unmet need within the past 12 months. Delayed (34.2%) and forgone care (23.1%) due to waiting time were most frequently mentioned. About 10% of the sample indicated forgone care due to distance and financial costs and about 6% reported delayed care due to distance. Finally, approximately 31% experienced at least one of the three types of forgone care in the past 12 months. If unmet need was indicated by the respondents, more than a half up to two-thirds reported a worsening of their own health as a consequence.
Table 3 shows the results of multilevel logistic regressions for delayed care due to waiting times and distance as dependent variables. All predictors and covariates were introduced simultaneously. Female sex (OR: 1.49 and 1.75), younger age (OR: 1.46 to 2.37), a statutory insurance (OR: 3.51 in case of waiting time), and low income (OR: 1.68 in case of distance) played a statistically significant role for reporting delayed care, while migration history and education were not significantly associated with delayed care. Social deprivation on the area level did not show notable variations.
Multilevel regression analyses of the reasons for forgone care are shown in Table 4. Young age was associated with reporting forgone care for any of the mentioned reasons. Female sex, lower income, and lower education were significantly associated with forgone care due to financial costs (OR: 1.54 to 2.57). Insurance status was particularly related to forgone care due to waiting time and distance (OR: 2.20 and 2.39). Indicating at least one of the three types of forgone care was significantly associated with being female (OR: 1.59), younger (OR: 1.60 to 2.49), less affluent (OR: 1.34 to 1.50), and statutorily insured (OR: 2.02). Social deprivation on the area level was related to forgone care due to distance (ICC: 0.018 and.
Finally, Table 5 shows the results of the multilevel logistic regression analyses with perceived health risk due to delay and forgoing as dependent variable. Participants with lower age and with an own migration history perceived a greater health risk due to delay (waiting time/distance). In terms of perceived health consequences due to forgone care (financial costs), only medium age showed a significant association. Odds ratios suggested clear trends, even though p-values did not indicate significance.
| (%)n | ||
|---|---|---|
| Sex(0) | Female | 1,009 (51.6) |
| Male | 946 (48.4) | |
| Age groups(0) | 18–40 years | 614 (31.4) |
| 41–59 years | 654 (33.5) | |
| ≥ 60 years | 687 (35.1) | |
| Migration history(34) | No | 1,479 (77.0) |
| 1st generation | 147 (7.6) | |
| 2nd generation | 295 (15.3) | |
| Education2(60) | High | 709 (37.4) |
| Intermediate | 595 (31.4) | |
| Low | 591 (31.2) | |
| Income3(288) | upper tertile (≥ 2250€) | 558 (33.4) |
| middle tertile | 560 (33.6) | |
| lower tertile (≤ 1625€) | 550 (33.0) | |
| Social deprivation4(5) | 1st quintile (least deprived) | 430 (22.1) |
| 2nd– 4th quintile | 1,125 (57.7) | |
| 5th quintile (most deprived) | 394 (20.2) | |
| Health insurance(3) | statutory | 1,698 (87.7) |
| private | 239 (12.3) | |
| Delayed care | (%)N | |
|---|---|---|
| Waiting time | No delay | 1,277 (65.8) |
| Delay | 663 (34.2) | |
| ThereofPerceived health risk | 356 (53.7) | |
| Distance | No delay | 1,821 (93.8) |
| Delay | 120 (6.2) | |
| ThereofPerceived health risk | 78 (65.0) | |
| Forgone care | ||
| Waiting time | No forgoing | 1,486 (76.9) |
| Forgoing | 446 (23.1) | |
| Distance | No forgoing | 1,753 (90.7) |
| Forgoing | 180 (9.3) | |
| Financial costs | No forgoing | 1,727 (89.2) |
| Forgoing | 209 (10.8) | |
| ThereofPerceived health risk | 132 (63.2) | |
| At least one type of forgone care | No forgoing | 1,340 (69.5) |
| Forgoing | 588 (30.5) | |
| Delay due to… | ||||
|---|---|---|---|---|
| waiting time | distance | |||
| Individual level | OR (95%-CI) | p | OR (95%-CI) | p |
| Sex (ref: male) | ||||
| female | 1.75 (1.42–2.15) | < 0.001 | 1.49 (1.02–2.18) | 0.04 |
| Age (ref: ≥60 years) | ||||
| 18–40 | 2.26 (1.70–3.01) | < 0.001 | 2.37 (1.39–4.06) | 0.002 |
| 41–59 | 1.46 (1.13–1.90) | 0.004 | 1.44 (0.86–2.41) | 0.162 |
| Migration history (ref: no) | ||||
| 1st generation | 1.08 (0.72–1.62) | 0.697 | 0.70 (0.30–1.63) | 0.407 |
| 2nd generation | 0.94 (0.71–1.25) | 0.666 | 0.82 (0.49–1.40) | 0.468 |
| Education (ref: high) | ||||
| intermediate | 0.92 (0.71–1.20) | 0.534 | 1.11 (0.69–1.78) | 0.661 |
| low | 1.01 (0.76–1.35) | 0.93 | 1.36 (0.80–2.30) | 0.258 |
| Income (ref: upper tertile) | ||||
| 2nd | 1.03 (0.80–1.33) | 0.836 | 1.01 (0.60–1.68) | 0.986 |
| 3rd | 0.94 (0.72–1.21) | 0.617 | 1.68 (1.05–2.68) | 0.03 |
| Health insurance (ref: private) | ||||
| statutory | 3.51 (2.33–5.30) | < 0.001 | 1.50 (0.72–3.08) | 0.273 |
| Area level | ||||
| Social deprivation2 | ||||
| Variance | 0.018 | 0 | ||
| ICC3 | 0.004 | 0 | ||
| MOR4 | 1.14 | 1 | ||
| Observations | 1,708 | 1,708 | ||
| Forgone care due to… | At least one typeof forgone care | |||||||
|---|---|---|---|---|---|---|---|---|
| waiting time | distance | financial costs | ||||||
| Individual level | OR (95%-CI) | p | OR (95%-CI) | p | OR (95%-CI) | p | OR (95%-CI) | p |
| Sex (ref: male) | ||||||||
| female | 1.64 (1.30–2.07) | < 0.001 | 1.39 (0.99–1.96) | 0.057 | 2.05 (1.50–2.81) | < 0.001 | 1.59 (1.28–1.97) | < 0.001 |
| Age (ref: ≥60 years) | ||||||||
| 18–40 | 2.95 (2.12–4.12) | < 0.001 | 2.55 (1.58–4.10) | < 0.001 | 1.68 (1.10–2.56) | 0.017 | 2.49 (1.84–3.36) | < 0.001 |
| 41–59 | 1.88 (1.38–2.56) | < 0.001 | 1.55 (0.99–2.43) | 0.058 | 1.42 (0.97–2.09) | 0.071 | 1.60 (1.22–2.11) | < 0.001 |
| Migration history (ref: no) | ||||||||
| 1st generation | 1.25 (0.80–1.94) | 0.334 | 0.88 (0.43–1.80) | 0.721 | 1.03 (0.55–1.92) | 0.925 | 1.24 (0.82–1.88) | 0.313 |
| 2nd generation | 1.14 (0.84–1.55) | 0.409 | 0.91 (0.57–1.47) | 0.711 | 1.44 (0.97–2.13) | 0.07 | 1.10 (0.82–1.48) | 0.521 |
| Education (ref: high) | ||||||||
| intermediate | 0.95 (0.72–1.27) | 0.746 | 1.55 (1.02–2.36) | 0.041 | 1.54 (1.04–2.28) | 0.033 | 1.16 (0.89–1.52) | 0.276 |
| low | 1.24 (0.89–1.72) | 0.198 | 1.42 (0.87–2.34) | 0.164 | 1.92 (1.25–2.96) | 0.003 | 1.35 (1.00-1.83) | 0.053 |
| Income (ref: upper tertile) | ||||||||
| middle tertile | 1.18 (0.88–1.57) | 0.27 | 1.37 (0.88–2.15) | 0.168 | 1.81 (1.19–2.78) | 0.006 | 1.34 (1.02–1.76) | 0.033 |
| lower tertile | 1.08 (0.81–1.44) | 0.613 | 1.51 (0.97–2.35) | 0.071 | 2.57 (1.70–3.88) | < 0.001 | 1.50 (1.15–1.97) | 0.003 |
| Health insurance (ref: private) | ||||||||
| statutory | 2.20 (1.40–3.44) | 0.001 | 2.39 (1.08–5.27) | 0.031 | 1.12 (0.64–1.98) | 0.69 | 2.02 (1.36–3.02) | < 0.001 |
| Area level | ||||||||
| Social deprivation2 | ||||||||
| Variance | 0 | 0.221 | 0 | 0.011 | ||||
| ICC3 | 0 | 0.018 | 0 | 0.002 | ||||
| MOR4 | 1 | 1.56 | 1 | 1.11 | ||||
| Observations | 1,683 | 1,683 | 1,683 | 1,683 | ||||
| Perceived health risk due to delayed care | Perceived health risk due to forgone care | |||
|---|---|---|---|---|
| (waiting time/distance) | (financial costs) | |||
| Individual level | OR (95%-CI) | p | OR (95%-CI) | p |
| Sex (ref: male) | ||||
| female | 1.17 (0.84–1.63) | 0.359 | 1.30 (0.61–2.64) | 0.523 |
| Age (ref: ≥60 years) | ||||
| 18–40 | 1.83 (1.15–2.87) | 0.01 | 1.61 (0.65–3.97) | 0.304 |
| 41–59 | 1.84 (1.20–2.83) | 0.005 | 2.91 (1.16–7.33) | 0.024 |
| Migration history (ref: no) | ||||
| 1st generation | 2.14 (1.08–4.23) | 0.03 | 0.65 (0.17–2.54) | 0.536 |
| 2nd generation | 1.02 (0.65–1.60) | 0.926 | 0.61 (0.26–1.43) | 0.253 |
| Education (ref: high) | ||||
| intermediate | 1.18 (0.78–1.77) | 0.436 | 0.46 (0.18–1.19) | 0.109 |
| low | 1.53 (0.95–2.46) | 0.082 | 1.08 (0.39-3.00) | 0.879 |
| Income (ref: upper tertile) | ||||
| 2nd | 1.28 (0.85–1.94) | 0.234 | 1.78 (0.62–5.11) | 0.283 |
| 3rd | 1.12 (0.74–1.69) | 0.585 | 1.80 (0.65–4.97) | 0.253 |
| Insurance (ref: private) | ||||
| statutory | 1.03 (0.47–2.23) | 0.943 | 3.83 (0.96–15.25) | 0.056 |
| Area level | ||||
| Social deprivation2 | ||||
| Variance | 0 | 0 | ||
| ICC | 0 | 0 | ||
| MOR | 1 | 1 | ||
| Observations | 613 | 183 | ||
Discussion
In the present study, magnitude, reasons, inequalities, and self-perceived health risks of unmet medical care need (delayed and forgone care) were examined among the general population in Germany. Delayed care was reported by 34% (waiting time), and 6% (distance) respectively. Prevalence of forgone care varied depending on the three different reasons (waiting time 23%; financial costs 11%; distance 9%). At least one of the three types of forgone care was indicated by 31% of the respondents. Significant social predictors for delayed care were female sex, younger age, as well as statutory insurance. Forgone care was associated with female sex, age < 60 years, lower income, lower education, and statutory insurance. Some differences were shown depending on the particular reason for forgone care. More than a half up to nearly two-thirds reported a perceived health risk due to delayed and forgone medical treatments. The perception of health risk was less pronounced among older participants. Area level social deprivation was shown to be particularly relevant for forgoing medical treatment due to distance.
Differences in methods and approaches used to assess unmet need among different surveys hamper comparisons with previous research. More precisely, these differences refer to the population considered, the range of health services covered, the reasons for unmet needs, the wording of questions, and the inclusion of delayed and forgone care (as opposed to forgone care only) in the definition of unmet need [6, 12, 13]. Comparisons across different countries have to be drawn carefully as country-specific aspects of the organisation of health care provision and the way in which vulnerable groups are protected from charges have to be taken into account [4]. In terms of Germany, patterns in the present study are in line with former research, even though prevalences of forgone medical care were higher in our study [5, 14, 28]. For instance, the EU-SILC survey considered the total population surveyed and not exclusively people with health care need. Furthermore, the questions of the EU-SILC survey in Germany referred to unmet needs for severe illnesses which also results in under-estimation of magnitudes and inequalities [6, 13]. Overall, clear social inequalities in unmet need and its predictors (sex, age, income, but less education) of former research from Germany was confirmed by the present study, and additionally, inequalities regarding health insurance were shown [4, 5, 28]. Similar to previous results from Germany, a main reason for unmet need was waiting time. This is still a highly discussed issue in Germany as patients with a SHI have to wait longer for an appointment than privately insured patients [43, 44]. Indeed, associations between migrant history and unmet need were hardly found which could be due to different sample characteristics.
The magnitudes, inequalities, and perceived health risks of unmet need suggest important health policy issues and call for action. The findings showed that economic factors such as affluence, place of residence, and insurance coverage are important drivers of observed disparities in our study. Furthermore, women and younger persons constantly reported higher rates of unmet need. The results vary depending on the reason for unmet need. In terms of delayed and forgone care due to waiting time, statutorily insured patients clearly reported higher unmet need. Previous research showed significantly longer waiting times for an appointment in the German outpatient setting for patients with statutorily health insurance irrespective of social status [50–53]. In this context, it has to be kept in mind that doctors are allowed to charge higher fees for privately insured patients, which potentially creates incentives for preferred treatment. Thus, abolishing the coexistence of SHI and PHI is a highly discussed topic which is supported by the majority of the population [54], and would promote more health care equity in terms of waiting time [55]. A study among statutorily insured people hardly found associations between forgone care and social status indicators, but particularly with perceived discrimination related to health care (e.g. waiting times) [32]. To reduce this discrimination of statutorily insured regarding waiting times, appointment service centres for medical appointments were introduced in 2019. First evaluations regarding specialist care revealed relatively low use, but the ability to make urgent appointments, with average waiting times significantly lower than the legally set maximum waiting period [56]. Improved education about different possibilities in the health care system to seek for timely treatment could increase the use of such services.
Forgone care due to distance was also associated with statutorily health insurance, and additionally with regional social deprivation in our study. Regarding the latter, it was shown that residing in more deprived urban or rural districts was associated with lower GP availability [34, 35]. Moreover, a study has shown that a higher proportion of privately insured people in a region was associated with higher GP and specialist density [38]. This suggests an unequal distribution of outpatient care to the disadvantage of deprived areas and statutorily insured patients which is an important issue in health policy research [33, 57, 58]. Different incentives and strategies (e.g. facilitating job opportunities for third-country physicians, improving promotion in medical faculties, raising consciousness in students for rural primary care when applying for university) were introduced to increase the number of physicians in these areas [59–61]. However, evidence about the effectiveness of such strategies is poor.
Forgoing medical care due to financial costs was not related to insurance status, but particularly to income. In Germany, financial and material circumstances are associated with access to and utilization of care, and thus, result in increased unmet need [31]. Financial burden due to out-of-pocket payments were much more pronounced among less affluent patients and to a lesser extent among SHI patients which facilitates delay and forgone care of necessary medical treatments [31, 62]. Generally, SHI covers a wide range of benefits that are the same for all those insured. The share of private out-of-pocket funding is moderate, even though co-payments are required (e.g. for prescribed or over-the-counter drugs and therapies). Moreover, a better education about costs and possible refunds could be helpful. Physicians’ representatives also worry about increased unmet needs and highlighted their requests including better financing, streamlining of administration, and faster implementations of reforms [63]. Moreover, an overall consistent association with female sex and young age may indicate that these sub-groups have a stronger awareness of access barriers. The findings provide information about the perception of health-related consequences of unmet need. High proportions of people who reported a worsening of their health due to delay or forgoing show the importance to take notice of delayed and forgone care in further health care system development. Finally, when putting postponed care in relation to health care costs, additional benefits were found when diminishing unmet need. Associations were found between forgone and delayed medical care and significantly higher health care expenditures among a heart failure population in the U. S [64].
Limitations of the study
There are some limitations of this study that have to be discussed. The sample was randomly drawn from a panel which was recruited offline. However, analyses were based on an online survey and only internet users and people with internet connection could be included. Furthermore, a selection bias cannot be ruled out as only about 39% of the invited persons participated. The distribution of social characteristics in our study compared to the general population was satisfying. Nevertheless, data was weighted by age, sex, federal state, and education according to the official statistics [40] using an iterative proportional fitting approach [39] to account for a potential bias. Moreover, analyses were restricted to individuals who were able to read German. This has to be especially kept in mind when evaluating results regarding migration history. Particularly, 1st generation migrants who recently immigrated may not be sufficiently represented which points to a general problem in public health studies [65]. Thus, associations with migration history could potentially be underestimated in our study. Moreover, the perception of delay and subsequent health risk may have been reported more or less times than actually occurred. Accordingly, a recall bias cannot be ruled out. Furthermore, we cannot distinguish between different health care providers and between severe and less severe health issues. The regression analyses regarding perceived health risk due to cost-related forgone care referred to a small sub-sample (n = 183). Thus, conclusions have to be drawn carefully. Finally, the ICC is quite low in some cases. However, small ICC values in logistic regression models are very common and do not necessarily indicate negligible effects [66].
Conclusions
A high proportion of respondents stated that they had delayed and/or forgone medically necessary treatment over a period of 12 months, and that they often perceived a worsening of their health due to this unmet need. The frequency of unmet need, the perceived worsening of complaints as a result of not seeking treatment, and the associations with indicators of individual and regional social inequality as well as health insurance suggest tailored interventions. Generally, social inequalities should be reduced as it was shown that for deprived people forgone medical care tended to be higher in countries with larger income inequalities, irrespective of average economic standard [11]. From a health policy perspective, equal access to health care for those in equal need is an important principle of equity [67]. Therefore, providing conditions in which those with equal needs have equal opportunities to access health care is expected to reduce unmet need among deprived groups. To this end, multiple implications are possible due to the comprehensive nature of health care access. When focusing on non-chosen, barrier-related unmet need, reducing waiting times (e.g. by further development of appointment service centres) and private co-payments as well as ensuring health care provision in deprived areas can contribute to a decrease of unmet need and potential health risks. The study provided a deeper insight into mechanisms of unmet need and its consequences. This included a variety of individual social characteristics and regional deprivation by using multilevel analyses. However, further supply-side and demand-side determinants of accessibility have to be considered. Conceptual frameworks of health care access highlight additional dimensions apart from availability and affordability which are limited to abilities to reach and pay [7]. In terms of dimensions like approachability, acceptability, and appropriateness, abilities to perceive, to seek, and to engage also have to be taken into account when analysing access to health care on the whole. More in-depth studies of these mechanisms and a disaggregated approach to analyse unmet need are required to include all dimensions of health care access. Furthermore, future research should rely on longitudinal data to control for further individual characteristics (e.g. changes in employment status and other live events, pathogenesis) that may contribute to the association between unmet need and health care utilization, and should include clinical and administrative data [2, 3]. Even if this cross-sectional study only deals with a section of the broad spectrum of accessibility, important factors and associations in terms of unequal access to necessary health care were identified.
Acknowledgements
Not applicable.
Abbreviations
Author contributions
OK and JK designed the study. JK conducted the analyses. JK interpreted the data and drafted the manuscript. DL made an essential contribution to data analyses and interpretation. OK and DL substantially contributed to interpreting the data and critically revised and approved the final manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL.
The work was carried out without external funding.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
All procedures were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The survey was approved by the Local Psychological Ethics Committee at the Center for Psychosocial Medicine, University Medical Center Hamburg (No. LPEK-0563). Participants gave their consent by starting the online survey. This procedure was chosen as participants were invited via email. The procedure was also approved by the Local Psychological Ethics Committee at the Center for Psychosocial Medicine, University Medical Center Hamburg (No. LPEK-0563).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
References
Associated Data
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.