Determinants of Hospital Readmission of Medical Conditions in Developing Countries

    

    

    Figure 3: Final thematic map showing two (2) main themes.
    
    

Socio demographic factors and hospital readmission

Socio demographic factors have been known to affect the health status of the general population, most especially the elderly and consequently readmission rates [10]. Socio demographic factors will therefore include age, gender, social support, education and employment.

Age: Age has been known to be associated with readmission rates [11]. Research done in Hong Kong revealed a high hospital readmissions rate among the young age group than the elderly [12]. increased bed occupancy [19]. On the contrary, findings from and majority was between the ages of 19-62 years [13]. More so, research done in Kenya among 1,148 children revealed the following results: 107 readmitted twice, 36 readmitted four times, 12 readmitted five times and 9 at least six times [14]. Furthermore, a study [15] conducted in Cote d’Ivoire revealed higher percentage (87.8%) of readmitted cases being younger (<44 years).

On the contrary, another study conducted among patients with heart failure in Nigeria revealed a significantly higher rate of readmission among the elderly [16]. This is also confirmed by a study conducted in South Africa which revealed that out of 406 patients readmitted, 45% were above 80 years [17]. Zhou et al. [18] in a study conducted in China also confirmed an increase in readmission rates among the elderly than the young age groups. The difference in readmission rate may be due to differences in gender and thus the need to consider the impacts of gender on readmission rate.

Gender: Gender as a non-modifiable factor is also known in many studies to have an impact on readmission rates. Most studies done in developing countries revealed high percentage of readmitted cases being males [12,15-17,19], this confirms a similar study [20] results done in a developed country. However no study revealed increase in readmission rate among women in developing countries. Some studies also reported the impact of gender to be mixed [21]. Male gender can therefore be seen in both developed and developing countries as a predictor of readmissions. In as much as gender is a non-modifiable factor, what accounts for increased risk of male gender to readmissions may be other socio economic factors.

Socio economic factors and readmission

Employment and education: Low socio economic status has been found to significantly increase the rate of readmission for an individual [21]. Studies conducted in some developing countries revealed high readmission rate among populations with low education, and unemployed [13]. Similarly study conducted in Cote d’Ivoire depicted a high rate (95.4%) of readmissions among individuals who were uneducated and 50.9% of readmissions among those who had no form of wage earned [15]. These studies are consistent with research conducted in some developed countries. The studies reported an increased risk of readmission among the unemployed or populations with low prestige occupations and less or uneducated populations as compared to privileged groups with high education and gainfully employed [23-25].

Social support: There is much evidence that, supportive social relations have an impact on health outcomes and that social support is essential for adjustment to illness [26].

An individual’s social support with regards to marital status, whether living alone or not has also been known to have an impact on readmissions prevalence [10]. Availability of family members, friends or social groups was also seen to reduce readmission rate [10]. Another study conducted in Hong Kong confirms how lack of social support increases an individual’s risk to readmission [27].

Conversely, some studies done revealed high rate of readmission among patients with social support. This is evident in recent study [28] which demonstrated a high readmission rate among patients with heart failure who had family support. Similarly, Hasan et al. [29] in a cohort study revealed that social support was less predictive of readmission. Controlling for social support and other socio demographic factors alone is not enough to reduce readmission rate unless other system related factors are also considered.

System related factors and readmission: System related factors can therefore be referred to as the availability, accessibility and coordination of care in the health care delivery system [30]. It is known that system related factors such as unresolved treatment of medical problems during previous discharge may contribute to readmissions [31].

Hospital policies

Length of Stay (LOS) and readmission: Hospital policies such as reducing patients LOS through premature discharge in order to reduce pressure on hospital bed is known to cause more harm than good as it is stated in recent studies that, reduction of length of stay was one of the consequences of high hospital readmissions [12,32]. In Western Cape Province in South Africa, a policy of reduction of LOS as an effective tool through an early crisis discharge policy to decrease bed pressure has rather led to high rate of readmissions, increase cost and increased bed occupancy [19].

On the contrary, findings from a research done in South Africa, Hong Kong and China respectively did not see reduction of length of stay as a factor for hospital readmission [17,18,33]. Similarly, Johnstone and Zolese [34] in a Randomised Controlled Trial (RCT) study posited that short LOS is not a marker of readmission rate if discharge planning is properly done.

In addition, findings reported from a research done in Nigeria showed that, 12.2% of 90 patients who had a short LOS were readmitted while the same percentage (12.2%) of 172 patients whose LOS was long were readmitted [16]. Thus, there was no difference between readmission rate among both short and long hospital stay patients. Halfon et al. [35] however in a study reported a positive correlation between long LOS and the risk for readmission.

Discharge planning: Many rehospitalizations are as a result of systemic failures in transition from hospital to the next source of care [27,36].

A who posited how insufficient dietary intake leads to malabsorption and diarrhoea with its associated negative effects thus increasing morbidity and mortality rates. More so, the quality of job was that, out of 564 patients discharged to sub-acute care, 40% had their test results pending and this is known to predispose patients to readmissions. Factors identified to be the leading cause of readmissions has been known to be; patient’s lack of knowledge regarding condition management, failure to consider post-acute care transition and lack of follow up visits [25].

Follow Up

Follow up is known to help in the reduction of readmission rate. This is evident in a pilot study conducted among diabetic patients which revealed a reduction of readmission after 2 to 5 days follow up visits were done post discharge [38]. Previous study [39] also supports follow up as a way of reducing readmissions and that was evident in a RCT study done. Additionally, a meta-analysis of 17 trials done depicted how home visit can help reduce readmissions and mortalities as well [40]. Patient education of condition management, predischarge assessment and domiciliary aftercare has been shown in a RCT as a way to prevent about 12% to 75% of all system related hospital readmission [41].

On the contrary, some studies conducted in developing countries did not show any significant reduction of readmissions among patients who had benefited from follow up visits after discharge from hospital [42].

Discussion

Age

Majority of the research done in developing countries [15,43-45] revealed high readmission rate among the young (<65 years) than the elderly (>65 years). This may be due to the fact that young ones are energetic and may involve in unhealthy activities which may worsen their conditions after discharge hence predispose them to repeated admission. Also, since the young may be in working force, they may not rest enough to recuperate from the sickness and may therefore return to work early so as to gain some income to support the elderly as supported by ICRW, where the youth working is seen as a source of families’ future well being in terms of economic necessities.

Additionally, the young, especially between ages 10 to 24 years, are often perceived as contributing to society’s problems, since they may engage in all forms of social vices which may put their health at risk.

More so, life expectancy of developing countries have been found to be low hence very few people live to the elderly age range (>65 years). An inference can therefore be made to suggest why the young (<65 years) have high readmission rate.

Conversely, some studies also confirmed a high readmission rate among the elderly [17,46,47]. This may be due to their decreased functional ability as supported by Yam et al. [48] who posited that aging population leads to increased burden of chronic diseases, multiple morbidities and disability and consequently increased in demand for healthcare. In addition, changes coupled with old age, such as change in smell and poor dental health, directly causes decreased in food intake which may also lead to decreased macro and micro nutrients and partly leads to poor health outcome [49] after discharge from hospitalization, thereby leading to rehospitalisation.

On the contrary, increase readmission among the elderly could also be due to the fact that elderly people report longstanding illness because they are more aware of their health and less resigned to limitations therefore prefers to report health complaints [50].

Gender

Findings suggested male gender to be a risk factor to readmission [12,15-17,51]. The association may be linked to social behavioral patterns commonly associated with male gender, such as delayed health seeking behaviours, often resulting in episodic use of health services by men [52].

Moreover, the reason may be due to the fact that men are expected to be the families’ bread winners. With this in mind, they work hard to fend for families even in their convalescent stage and this may affect their health negatively resulting in possible rehospitalisation. This is supported by Vlasoff who demonstrated how men have a greater involvement in paid labour force. Moreso, men usually engage in strenuous and unskilled occupations which predispose one to readmissions after discharge as confirmed by Verbrugge [53] who posited that men engage in more toxic occupational exposure than women.

Furthermore, men are perceived to be strong in some cultures and so are inhibited from verbalising minor ailments. This makes some men to harbour symptoms until it becomes severe resulting in increased rehospitalisation. This is supported by Courtenay and keeling [54] who stated that optimistic perception of health status by men makes them place lower value on preventive care hence compounding problems which consequently leads to readmission. In addition, a study [55] establishes that men report less help seeking behaviours and engage in unhealthy lifestyle behaviours such as smoking and alcoholism which all worsen conditions leading to excess use of hospital services. Women, however, may not have increased readmissions due to the nature of work engaged in, lifestyle and the early reporting of even the minor symptoms to health facility [53]. Moreover, studies that depicted impact to be mixed among gender could be due to other factors such as socio economic or system related factors.

Employment

Findings revealed a positive correlation between employment and readmission rate. Individuals unemployed had high a high readmission rate. According to Wilkinson and Marmot [56], unemployment puts one’s health at risk and causes more illness which may consequently lead to increase readmission. For instance, an unemployed person may not have money to purchase prescribed medications and hence condition may deteriorate without discharged medications. More so, an individual who is poor may not be able to afford a balanced diet, thus will have poor nutritional status which will further reduce their immunity and lead to poor health outcomes even if discharged medications are available. This is supported by [56,57] who posited how insufficient dietary intake leads to malabsorption and diarrhoea with its associated negative effects thus increasing morbidity and mortality rates.

More so, the quality of job was seen in a study as also imperative to good health and this is supported by Wilkinson and Marmot [56]. This could be due to insecurity of job making one feel threatened coupled with psychological problems such as stress or anxiety which may impact on health negatively with probable readmission.

Education

Educational attainment is positively correlated with wealth and knowledge level with regards to disease management [58]. Uneducated individuals may therefore not have enough knowledge with regards to disease management and may resort to improper or unsafe ways of condition management such as seeking spiritual help and then reports to the hospital as a last resort. In Africa, for instance, uneducated individuals engage in superstitious beliefs by trying alternatives, such as traditional healers, and often visit the hospital as a last resort [16]. This health seeking behaviour worsens condition and may lead to readmissions. This is confirmed in a recent study [59] conducted in Uganda which indicated how lack of formal education had an association with all-cause mortality among PLWHA.

Social support

Studies [26,27,60] depicted an inverse relationship between social support and readmission rate. From the studies, social support can be seen as a significant predictor of readmissions because social support provides emotional care, access to material goods or services and also information and advice on condition as supported by Luttik et al. [26], thus, the reason for high readmission rates among individuals who were socially isolated.

Also, presence of social support may enable early detection of any changes in conditions as confirmed by Luttik et al. [26].

In addition, available social support may protect patients from pathogenic influence of stress and thereby perceiving stressful events as less threatening hence, minimising the incidence of complications that may have been precipitated by stress Luttik et al. [26]. However, the fact that readmission rate was low among individuals with social support does not mean conditions did not recur, but it could be that health service was sought at other avenues such as native doctors and pastors.

Conversely, some studies also demonstrated a high rate of readmission among patients with social support [28,29]. This could be due to caregiver stress on patient as supported by Luttik et al. [26]. Similarly, Wilkinson and Marmot [56] posited that bad close relations can lead to poor health outcomes. For instance, a cargiver for an Asthmatic who smokes often in the patient’s presence may have an impact on the condition of the patient and consequently increase chances of readmissions. Moreover, presence of family members may encourage minor exacerbations reporting and may lead to increase readmissions.

On the other hand, presence of some family members may discourage reporting conditons early to hospital but rather resort to other alternatives with the hospital being the last option as supported by Ogah et al. [16]. When this happens, conditions become deteriorated and may need readmission when reported back to the hospital.

Social support could not have any effect on readmissions as seen in a study [29]. This may be due to the different ways social support was operationally defined. Some studies may conceptualize social support as living alone or not [61], while others may refer to being married or not [60], and other studies may refer to how one is able to get physical support, information or advice [12,29]. Conceptualising social support in different ways may give different results. For instance, if social support is defined as living alone or not, someone may be living alone but may have a close friend whom he or she can lean on for help at all times. In this case, the result may depict low readmission rate among individuals living alone when in actual sense the individual has social support.

Hospital policy (LOS)

Studies revealed short length of stay as a factor for high readmission rates [12,32]. Considering the results from the studies, reduction of length of stay as a policy will not allow for full recovery of a patient’s condition nor allow for adequate preparation for discharge thereby leading to increased readmission as supported by Segal, Akutsu and Watson [62].

Conversely, short length of stay was not seen as a predictor of hospital readmission in another study [34]. This could be due to factors such as the execution of efficient discharge planning, timely and regular follow ups, and the ability to procure the required discharged medications.

A study [35] which depicted high readmission among patients that had a long hospital stay could be attributed to hospital acquired infections, like influenza that was not detected during admission. However staying long in the hospital may not necessarily mean that discharge planning was done effectively and hence likely increase in readmission.

Further studies [12,17,18] did not find any correlation between length of stay and readmission rate. This result may be due to differences in hospital types and study populations [19].

Discharge planning

Findings revealed how poor discharge planning led to increased readmission rates [37]. Factors identified, such as lack of adequate knowledge about a condition, may lead to increased readmission. For instance, if a diabetic patient is shown how to give insulin to control increased blood glucose levels without being educated on the management of low glucose level, one may report in a somatic state. This could have been prevented if adequate information was given. Improper discharge planning done may lead to the inability to detect medications on discharge, there by leading to recurrence of condition which may be worsened and warrant readmission [63].

Follow Up

Majority of the studies conducted depicted an inverse relationship between follow up and readmission rate. Follow ups may help to address unmet needs, detect abnormalities early and advice given before condition becomes severe to merit further admission. Knowledge about condition management is being reinforced, medication adherence confirmed, laboratory results are reported and the needed action taken accordingly during follow up.

Alternatively, a study did not see any significant reduction in readmissions when follow ups were embarked. Reasons could be that the follow ups were not done timely and regularly. This is supported by Wong et al. [16] who posited that most readmissions occur within two weeks and this two week ‘’out- time’’ is a high risk time frame where follow ups should be intensive and regular. The result from the study could be due to the fact that the follow ups were done late when wrong interventions had already been done. For instance, when wrong dosage has been taken and medication is almost about completion. Also, it could also be that the health personnel had limited knowledge about the discharge summary of the patient. This is why it is important for care pathway to be connected from hospital to community [12], so as to have adequate information about patient being discharged.

Early discharge planning, patient centered care, telephone follow up and multidisciplinary hospital based quality initiative were found to reduce readmissions. The possible reason why early discharge planning led to reduced readmissions may be the ability to identify early, patients that needed special planning in the course of hospitalization as supported by Inui et al. An inference that can be made regarding patient centered care is the fact that individuals tend to have a sense of power if they are involved in care and are likely to adhere to discharge instructions. This is supported by Baum [64] who stated that projects which involved active participation of those involved in the issue proved high success rate and was sustained (

  

    
Factors
    
Low Readmission Rate
    
High Readmission rate
    
No correlation
  
  

    
Elderly
    
Chan et al. 2011, Ve et al.    2010, Snow et al. 2000, Gbiri et al. 2011
    
Ogah et al. 2014,    Schuurmansstekhoven, Whitelaw & Devonmeiring 1992, Zhou et al. 2014
    

  
  

    
Gender (Male)
    
Verbrugge (1985)
    
Chan et al. 2011, Niehaus et    al. 2008, Ogah et al. 2014, Schuurmansstekhoven, Whitelaw & Devonmeiring    1992, Ve et al. 2010
    
(Herzog 2003)
  
  

    
Unemployment and uneducated or    lack of education
    

    
Ve et al. 2010, Burke et al.    2014, Jasti et al. 2008, Rubin et al. 2014
    

  
  

    
Availability of Social Support
    
Luttik et al. 2005, Yam et al.    2010, Ve et al. 2010
    
Schwarz & Elman 2003, Hasan    et al. 2010, Ogah et al. 2014
    

  
  

    
Short LOS
    

    
Chan et al. 2011, Hammond,    Pinnington & Phillips 2009, Segal, Akutsu and Watson (1998)
    
Johnstone & Zolese 1999
  
  

    
Long LOS
    

    
Halfon et al. 2002
    

  
  

    
Poor discharge planning
    

    
Walz et al. 2011, Alper &    O’Malley 2013
    

  
  

    
Implementation of follow up
    
Seggelke et al. 2014, Elkan et    al. 2001, Benbassat & Taragin 2000
    

    
Wong et al. 2008
  

Table 2: Summary of differences and similarities among studies reviewed.