Central Journal of Family Medicine & Community Health

Cite this article: Chowdhury SR, Bohara AK, Tamrakar D, Drope J, Karmacharya B (2017) Distance and Lack of Information as Barriers to Cancer Screening: Spatial Empirical Evidence from Nepal. J Family Med Community Health 4(6): 1128.

Abstract

In the absence of a national cancer registry system in Nepal, the objective of our study is to bring in current cancer incidence data through a primary survey across five different cancer hospitals of Nepal. The study helps in understanding the prevalence of cancer and also in examining the barriers to proper utilization of cancer preventative measures. Through cluster analysis using Local Moran’s I and Getis-Ord G*, we have identified hot spots of cancer cases across Kathmandu Valley, the central development region of Nepal. The spatial analysis implies the presence of clusters of cancer incidences in that region. Finally, through regression analysis, we tried to quantify the impact of distance and accessibility to information on patients’ likelihood to screen for cancer. We ran three different specifications each of Probit and Negative Binomial model to establish the relationship. From our empirical regression analysis, we found that if a patient had to travel a distance of more than 10 hours to avail medical services, it would significantly reduce their likelihood to screen. Whereas any informational, intervention had significantly increased the probability of undertaking cancer screening tests. Other socio-economic factors such as income and employment status also play important roles in medical care utilization. The results are uniform across both the probit and negative binomial models.

*Corresponding authorSoumi Roy Chowdhury, Department of Economics, University of New Mexico, 1915 Roma Ave NE, Albuquerque, NM 87131, USA Tel: 1-505-277-5304; Email:

Submitted: 03 June 2017

Accepted: 29 August 2017

Published: 31 August 2017

ISSN: 2379-0547

Copyright© 2017 Chowdhury et al.

OPEN ACCESS

Keywords•Cancer•Screening tests•Nepal•Cluster analysis•Information

Research Article

Distance and Lack of Information as Barriers to Cancer Screening: Spatial Empirical Evidence from NepalSoumi Roy Chowdhury1*, Alok K. Bohara1, Dipesh Tamrakar2, Biraj Karmacharya2, and Jeffrey Drope3

of Economics, University of New Mexico, USA2Department of Community Medicine, Kathmandu University, Nepal3Economic & Health Policy Research, American Cancer Society, USA

INTRODUCTIONThe global burden of cancer is increasing at an alarming

rate. American Cancer Society reports the mortality rate of cancer accounting for one in every seven deaths worldwide [1]. The figure is higher than HIV/AIDS, tuberculosis, and malaria combined. The burden is expected to increase further in future to the point that the year 2030 can see 21.7 million new cancer cases and 13.0 million cancer deaths worldwide [1]. Proper action towards prevention of cancer mortality is difficult to implement in most of the developing countries given the unavailability of cancer registry system [2]. A cancer registry will not only display the demographics of the patients but will also give an overview of the barriers and impediments that cancer patients are facing. Understanding these obstacles may help in developing policies that are essential to reduce mortality. Incidence data for countries that do not have any comprehensive cancer registry management system are compiled by IARC through published reports and articles based on case studies and pilot surveys.

Most of the South Asian countries such as Maldives, Nepal,

and Afghanistan fall into this category of not having any adequate cancer registry system of their own. However, these countries have timely collected regional and case study based information to keep a record of their cancer incidences [3-6]. A goal towards cancer prevention requires epidemiological information on various individual habits that give rise to the disease, demography of cancer patients, types and patterns of cancer prevalent among the masses, and available resources to decrease the risk of it. It is also imperative to know about the barriers faced by an individual in proper utilization of those resources.

A standard method of cross country comparability of World Health Organization (WHO) revealed that chronic diseases had accounted for 42% of all the deaths in Nepal, and 7% of which was related to cancer alone [7]. Similar studies on Nepal which contributed significantly to understanding the cancer prevalence can be found elsewhere [2,8,9]. A notable study [8] has collected cancer incidence data from seven major hospitals of Nepal. They found that for males, lung cancer is the most prevalent one (14.6%) followed by the oral cavity (7.8%) and stomach

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cancer (7.5%), whereas cervix uteri (21.7%), and breast (15.7%) cancers are the most frequent ones among females. Their data is limited to demographic information of the cancer patients with details on their sex, age, occupation, religion, diagnostic methods, and location. There are other sets of literature that examine the inequalities in access to cancer treatment and the role of socio-demographic factors on the survival outcomes of cancer. It is found that highly educated individuals utilize specialized cancer treatments to a greater extent than the less educated patients [10]. Similarly, patients with socio-demographic disadvantages and cultural barriers are more likely to exhibit poor indicators when it comes to diagnosing of cancer [11-13]. But the literature lacks in their empirical findings regarding the role of distance and information in determining the inequalities in access to treatment. The association of distance and treatment uptake of cancer patients has been studied before in other countries. Some of these studies found no significant difference in outcomes for patients living nearby the cancer centers compared to those living far [14,15]. However contrasting studies showed that survival outcomes get worse for cancer patients living far away from the comprehensive cancer care, [16,17]. Even regarding overall utilization of health care services, distance plays an ambiguous role. For instance, no significant role of distance in health service use is found in [15], but others found that overall health outcomes of an individual get poorer when they are unable to access health services due to higher monetary and time cost [17]. This prompted us to study the effect of distance in the uptake of cancer screening facilitiesin Nepal. Distance plays out relevant for Nepal because of the varying terrain of the country.However, it is not only the distance but for a developing country like Nepal, the major obstacle to cancer screening and diagnosing may rest in the lack of awareness regarding the availability of such facilities. The absence of information and ignorance prevents them from undertaking recommended screening measures which are instrumental in early stage diagnosis of cancer and hence cancer survival [18]. To ensure that effective information is given to the patient and their families, the healthcare providers should assess the informational need of the patient and make sure the dissemination of the same [19,20]. In the absence of proper awareness, any information that may come from hospital on screening measures will be regarded as recommended by the individuals.

In the backdrop of previous research, we extend the current state of knowledge regarding cancer incidences in Nepal. Specifically, in our paper, we tracked patients from five different cancer treatment centers (a) to study their individual behavior regarding utilization of available cancer screening procedures before getting diagnosed with the disease, and (b) to identify factors that are impediments to such utilization.

We do cluster analysis and hot spot analysis to see if the incidences are concentrated around a particular geographic location of the country. Also, using regression strategies, we investigated the effect of distance and the role of information on regular cancer screening practices of individuals.

MATERIALS AND METHODS Survey and sampling strategy

Primarily, seven hospitals in Nepal cater to most of the cancer patients of the country; they are mostly located in the southern and central region of Nepal [8,21]. Previous literature highlighted the roles played by Bir Hospital, Bhaktapur Cancer Hospital, B.P Memorial Cancer Hospital of Chitwan, and Teaching Hospital of Tribhuvan University in cancer management of the country [22]. Due to the importance and substantial inflow of cancer patients to these hospitals, our survey was administered to patientsfromBir Hospital, Birendra Military Hospital, Bhaktapur Cancer Hospital, Dhulikhel Hospital, and B.P Koirala Memorial Hospital.1 The study was approved by Institutional Review Board of University of New Mexico, USA, Nepal Health Research Council (NHRC), and from the Institutional Board of Dhulikhel Hospital, and Kathmandu, Nepal. The survey comprises of 650 new cancer patients who are 18 years and older.2 The cancer patients are randomly selected to be interviewed for the survey over a span of three months. All the hospitals except B.P Koirala Memorial Hospital were surveyed from December 2015- February 2016. However, patients from B.P Koirala hospital were interviewed over a span of 2 weeks in April 2016.

Enumerators involved in data collection process visited the hospitals to track any new cancer patients (both inpatients and outpatients) over the survey period. The total number of patients selected for interview from each hospital was dependent on the volume of new cancer patients that a particular hospital receives in a cycle of treatment. The survey contains information ranging from socio- demographic characteristics of the patient, their knowledge on various aspects of cancer, awareness about screening behaviors, mental and economic burden faced by them, and the different coping strategies including familial support that the patients experience throughout the process of treatment. The data were analyzed using Stata 13 software.3 We have also used ArcGIS software to undertake the spatial analysis.4

Spatial methods of analysis

The spatial analysis using the ArcGIS software analyzes the distribution of cancer incidences and types of cancers in Nepal. The reported cancer incidences are plotted in a Nepal shape file to show the spread of cancer cases across different geographical locations. Further, we have studied the spatial concentration of cancer cases to find out if there are clusters of cancer incidences in the country. Identifying spatial patterns are important from

1The teaching hospital of Tribhuvan was approached for the study, but the fact that all cancer patients referred to either Bir or Bhaktapur made it redundant to include it in the survey. 2We have dropped 50 cancer patients from the analysis due to incomplete information. We have interviewed only the adults and not children.

3 StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP

4 ERSI 2001. ArcGIS Desktop: Release 10. Redlands, CA. Environmental Systems Research.

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the perspective of public policy making since it helps in creating health facilities specific to these locations. A grouping of a higher number of cancer incidences or spatial autocorrelation in a geographic space is called a cluster. The most commonly used indicators of spatial auto-correlation are Moran’s I and Getis Ord G-statistic.5 A positive value for Moran’s I indicate that a VDC has a neighboring VDC with similar values and are part of a cluster. This suggests that both the VDCs are spatially auto correlated. Whereas, a negative value indicates the absence of clusters. Under Local Moran’s I, we test the null hypothesis that there is no spatial clustering.

Empirical strategies

In the empirical section of the paper, we analyze factors determining the uptake of health care utilization. Precisely, we studied the screening behaviors of patients before they got diagnosed with cancer. Intention to screen for any possible medical conditions is guided by the av6ailability of the services, travel time, and the knowledge about the importance of screening.

Variables of interest: Individuals are asked if they used to do routine checkups (Regular Screening) before getting diagnosed with cancer which reflects their adaptation of preventative strategies. Regular screening refers to the practice of undergoing screening tests for the most popular types of cancer at a regular interval of time by any individual. This is done to be able to detect the disease at their early stages. There can be different screening procedures for various cancer types. However, most of the screening tests are not easily available in all the hospitals. Only selected super-specialty cancer hospitals of Nepal have the screening procedures available to be used by the individuals. The fact that most of these tests are not readily available in the country puts the current study in perspective. If they were readily available in most of the medical centers, the uptake rate of screening would have been higher with no significant barriers in accessing those facilities. Also, individuals would have been more aware of the existence of such tests. We briefly describe some of the preventative mechanisms available in selected hospitals of Nepal for different types of cancer. Along with Visual Inspection with acetic acid, Pap test is also available in some of the specialty cancer hospitals such as B.P Koirala Memorial hospitals [23] and Bhaktapur Cancer Hospitals [24] to test for cervical cancer. Mammogram remains the most common diagnostic test for breast cancer even though it is available in only a few centers in the country [25]. Endoscopy to screen for colorectal cancer is prevalent in most of the developing countries, but Colonoscopy remained the gold standard for screening colorectal cancer and is practiced in limited colonoscopy centers of Nepal [26,27]. The lung cancer screening tests such as CT scan also gets practiced in some hospitals of Nepal [28].

5The main difference between these two statistics is that for Local Moran's I the local mean is calculated with the number of incidences of neighboring VDCs, unlike in the case of Getis Ord G-statistic where the local mean includes the number of cancer cases of the concerned VDC along with the other adjoining VDCs.

6 Over dispersion in data means refers to a condition where variance is greater than the mean of the distribution.

Individuals who practiced routine check-ups were asked about the number of times they have availed those services (Screening counts). Distance is measured by the number of hours a patient travel to reach to the nearest cancer facility hospital. Distance is measured by a categorical variable where the base category is any distance below three hours, and the rest of the categories are between three to five hours, five to ten hours, and more than ten years respectively. Information is a binary variable indicating if patients have received any visual information from hospitals through posters or another medium of communication on the need and importance of screening. Apart from these two primary independent variables of interest, we also include other socio-demographic factors. Income is a categorical variable with the base category of income is below 20,000 in Nepali rupees (NRS). Other subsequent categories are NRS 20,000- 30,000 and NRS >30,000 respectively. Similarly, patients are divided into three educational groups, no education (no formal school enrollment), some form of education (those who studied up to Class 8), and educated (for educational attainment of Class 9 and above). Other control variables include the age of the patient, employment status, and ethnicity.

Econometric model: For our binary dependent variable of whether a patient has screened before or not, we ran probit regression models with split and full samples. Under the full sample model, we included all the cancer patients irrespective of gender. Whereas in the subsequent models, we limited our sample to only females, and to specific types of cancers.

A Probit model is estimated under the normality assumption of the error term and is given in Equation (1) as follows:

*0 1 21

0 (1)i ij i i i

iifScreen Distance Information X

RSotherwise

β β β = + + + +=

Where ( ) 1iRegularScreening RS = if the respondent (i) has screened for cancer at least once before getting diagnosed; zero otherwise. Due to the latent nature of the binary dependent variable, marginal effects are calculated to assess the impact of the explanatory variables on the probability of screening uptake. Further, to approximate the intensity of screening rate, we asked the patients about the number of times they have availed the screening services. A negative binomial model is used where the dependent variable measures the Screening counts. The negative binomial model appears to be an appropriate count model which relaxes the equidispersion assumption of Poisson regression techniques to allow for real life circumstances of over dispersion in data [29].7

RESULTS

Spatial distribution of cancer incidences and types of cancer

In this section, we project the disease profiling of cancer

7 There are (n=103) patients who practiced routine screening before getting di-agnosed with cancer. Of them, (n=37) patients did not know about their stage of diagnosis. Remaining (n=44) patients got diagnosed at their initial stages of can-cer, and (n=24) patients got diagnosed at their advanced stage. Thus, with some limitations with the data, we see that majority of the patients who availed routine screening of cancer are diagnosed early in their disease state.

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incidences. We primarily focused on (a) The spatial distribution pattern of cancer incidences and type of cancers in Nepal (b) The spatial concentration of cancer cases and identifying clusters and hot spots of cancer incidences across the country.

The ArcGIS Map (Figure 1 (A) and Figure 1 (B)) given below shows five development regions of Nepal. The purple dots are the location of those hospitals included in the survey.

Figure 2 gives the spatial distribution of cancer incidences in the country as reported during the survey period. There are a large number of VDCs located close to the central region of the country with incidences of cancer cases, whereas not many cases have been reported from the hilly region. Kathmandu Municipality reports the highest incidence of cancer case followed by Lalitpur and Bhaktapur municipalities. This may be a result of self-selection bias since all the cancer hospitals surveyed for the study are located in the central region. This can also be due to the higher accessibility of medical services in the central region. The ease of access to medical treatment in this region may have resulted in more number of cancer incidences getting reported. In other words, the distance needed to travel by the patient from the nearest cancer facility may have played a significant role in the utilization of medical services. Figure 3 below gives a spatial distribution of all the different types of cancers recorded during the survey period.

In Table 1, we have shown the distribution of cancer cases categorized by gender. The most commonly identified cancer is breast cancer (28.88%) followed by Lung & trachea cancer (18.85%) among female and males respectively. Other prevailing cancers among the male population are Stomach, Head and Neck, and Colon, whereas, among the female population, they are Cervical, Lung, and Head and Neck. We acknowledge the fact the brain cancers are primarily different from that of head and neck cancer. However, given a negligible number of patients (n=4) in our sample suffering from brain cancer, we coded the two types of cancers together. The spread of various types of cancer found in our survey complements the estimates of previous studies [8].

Spatial concentration of cancer cases in Nepal

Identifying spatial concentration requires finding out clusters of reported cancer cases. Using our data, we found five VDCs all located in the central region with high-value clusters (Figure 4). We did not find any significant presence of low-value clusters or any outliers. However, for most of the VDCs, the incidence of cancer cases was observed to be random and did not exhibit any presence of clusters.

We extended our cluster analysis to identify geographical areas with statistically significant high values (hot spots) and low values (cold spots) of cancer incidences. Identifying hot spots and cold spots is done using Getis-Ord Gi* tool which examines if the observed spatial clustering of high and low values are distributed randomly. As can be seen in Figure 5, there is a significant presence of spatial clustering of high values. For a statistically significant hot spot, a VDC with a higher number of cancer cases is needed to be surrounded by other high valued VDCs. We have

Figure 1 (A) Outline map of Nepal with five development regions. (B) Outline of the central region of Nepal.

Figure 2 Cancer incidences in Nepal by VDCs.

identified such hot spots in Kathmandu Valley where the clusters are significant at 90, 95, and 99%.

Descriptive statistics

Table 2 describes the variables used in the study. We find that17.58% of the patients used to practice regular screening test for cancer before they got diagnosed with the disease8. We wanted to see how much does screening behavior is affected

8 As of August 2017, 1 US$ = 102.49 NRS.

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Figure 3 Different types of cancer diagnosed in Nepal.

Figure 4 Cluster Analysis [Local Moran’s I].

by geographical barriers and informational accessibility. Majority of the patients (44%) resides within three hours of distance to their nearest cancer hospital; whereas 21% of the sample came from places which required more than 10 hours of travelling time. Regarding informational accessibility, about 22% of the patients have seen displays and posters in hospitals with relevant information about different screening methods, the recommended ages, and the benefits of screening. These displays may act as an informational intervention to the general population making them aware of available facilities. Our sample represents a poorer section of the society with 84%of individuals reported an income level less than NRS 20,000, and 52% of the patients were unemployed at the time of the survey9. Similarly, on

9 Table 3 gives the probit regression coefficients whereas Table 4 is the mar-ginal probability effects of the probit model.

the educational front, 59% of the surveyed population reported not having any basic education and 20% have an educational level above Class 9. There is a uniform representation of all the ethnic groups with Brahmins and Janajati representing the largest groups.

Probit regression results

The probit regression results are presented in Table 3 and Table 49. We ran three different model specifications. In Model 1, we study the screening behavior of all the patients (full sample analysis) irrespective of gender and types of cancer. Whereas, in the subsequent models (Model 2 and Model 3), we have looked

10 Distance is significant at 12% in the third model of Cervical and Breast can-cer patients. The insignificance may be caused by a significant drop in sample size for the final model.

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Figure 5 Cluster Analysis [Getis Ord Gi* Statistic].

Table 1: Types of cancers by gender.Cancer Types Male: Numbers (%) Female: Numbers (%)Lung & Trachea 49 (18.85) 39 (12.11)Breast - 93 (28.88) Stomach &Oesophageal 41 (15.77) 21 (6.52)Head , Neck & Brain 39 (15) 29 (9.01)Cervical - 59 (18.32)Colon 29 (11.15) 17 (5.28)Prostate 8(3.08) -Bladder 11 (4.23) 14 (4.35)Oral & Nasopharynx 24 (9.23) 10 (3.11)Pancreatic 5 (1.92) 3 (0.93)Blood 19 (7.31) 12 (3.73)Others 35 (13.46) 25 (7.76)

Table 2: Socio-Demographic Characteristics on Routine check-up for cancer.Variables Description Mean SD Min MaxRegular Screening Have done screening before getting diagnosed for cancer. [Yes=1] 0.17588 0.38104 0 1Screening Counts Number of times got screened for cancer 0.27471 0.66169 0 4 Distance: Time needed to reach the nearest cancer hospital Distance: < 3 Less than 3 hours[ Base variable=1] 0.44389 0.49726 0 1Distance : 3-5 Between 3- 5 hours 0.13568 0.34273 0 1Distance : 5-10 Between 5-10 hours 0.20268 0.40233 0 1Distance: >10 Greater than 10 hours 0.21273 0.40958 0 1Information Have received any information from hospital on cancer. [ Yes =1] 0.22111 0.41534 0 1Income: <20k Income <NRS 20,000[ Base variable =1] 0.84255 0.36453 0 1Income: 20-30k Household income NRS 20 - 30,000 0.1072 0.30963 0 1Income: >30k Household income > NRS 30,000 0.04523 0.20797 0 1No Education No primary education [ Base variable =1] 0.59129 0.49201 0 1Education: Till Grade 8 Have studied till Class 8 0.19933 0.39983 0 1Education: Grade 9-12 Have studied more than Class 9 0.20436 0.40357 0 1Age Age of the patient 52.3704 14.0834 18 89Female Patient is a female [ Yes =1 ] 0.54606 0.49829 0 1Unemployed Patient is unemployed [ Yes =1] 0.52931 0.49956 0 1Brahmin Brahmin [ Base variable=1 ] 0.24623 0.43118 0 1Chherti Chhetri =1 0.17085 0.3767 0 1Newar Newar=1 0.16918 0.37522 0 1Janajati Janajati =1 0.24791 0.43216 0 1Madhesi_Dalit Dalit =1 0.1608 0.36766 0 1

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specifically into female cancer patients, and breast and cervical cancer patients respectively. The primary variables of interest such as Distance and Information are found to be significant in all the three models10. We can see from Table 4 that as Distance to the nearest cancer hospital increases by more than10 hours, it significantly decreases the probability of routine check-up at a range of four percentage points to ten percentage points depending on the specification used. Information has significantly highest impact on the behavior of screening. It is important that patients are given timely information on the types of screening tests available and procedures to avail them. Patients who have received any form of information from the hospital are

significantly highly likely to undergo any screening tests at a range 19.4 to 22.9 percentage points. Importantly, socio-demographic factors also play a crucial role in their decisions to screen as well. Individuals in the highest income bracket of NRS >30,000 are significantly more likely to get screened. Specially for the breast or cervical cancer patients, it appears that the richest section of the sample are 36 percentage points more likely to get screened. Education seems to have not played any major role in determining screening behavior. The lack of significance for the highly educated individual may have reflected some multicollinearity between Information and Education. Apart from the above mentioned factors, we find that the female patients

Table 3: Probit Regressions: Role of Distance and Information on the likelihood of screening for cancer.

VariablesAll Female Breast & CervicalRegular Screening Regular Screening Regular Screening

Distance: 3-5 0.0223 0.0615 0.596(0.18) (0.30) (1.42)

Distance : 5-10 0.348** 0.164 0.114(2.17) (0.99) (0.52)

Distance: >10 -0.395** -0.477*** -0.254 (-2.11) (-3.42) (-1.26)Information 0.848*** 0.856*** 1.072*** (11.15) (5.41) (7.53)Income: 20-30k 0.140 0.725 0.372

(0.63) (1.60) (0.31)Income: >30k 0.293*** 0.677 1.211*** (2.65) (1.54) (2.83)Education: Till Grade 8 -0.165 -0.238** -0.649***

(-0.87) (-2.38) (-2.60)Education: Grade 9-12 0.149 -0.0602 -0.396 (0.62) (-0.23) (-0.85)Age 0.00927 0.00919* -0.0229*** (1.26) (1.81) (-3.85)Female 0.481* (1.94)Unemployed -0.198 -0.301*** -0.522*** (-1.27) (-2.95) (-2.80)Chherti -0.0172 0.0488 0.269

(-0.06) (0.17) (0.56)Newar 0.0792 0.00352 0.106

(0.45) (0.01) (0.92)Janajati 0.236*** 0.118 -0.0439

(3.07) (0.71) (-0.16)Madhesi_Dalit 0.172 0.347*** 0.195

(0.72) (6.41) (0.56)

Constant -1.989*** -1.398*** 0.139

(-3.74) (-5.05) (0.28)N 594 326 152Log_Likelihood -245.7 -146.5 -59.68AIC 499.4 300.9 127.4BIC 517.0 316.0 139.5t statistics in parentheses * p<0.1 ** p<0.05 *** p<0.01Abbreviations: Distance: 3-5: It takes 3- 5 hours to reach the nearest cancer hospital; Distance: It takes 5-10 hours to reach the nearest cancer hospital; Distance: It takes >10hours to reach the nearest cancer hospital; Income: 20-30k: Household income is between NRS 20 - 30,000; Income: >30k: Household income is greater than NRS 30,000; Education: Till Grade 8: The patient has studied till Grade 8; Education: Grade 9-12: The patient has studied more than Grade 9.

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are 11 percentage points more likely to screen than the males’ counterpart. We also looked into the employment status of patients as a deciding factor for their decisions to screen. Being unemployed, decreases the likelihood of screening by 7.5 -11.2 percentage points.

Negative binomial regression results

The probit regression results are reinforced by the results from negative binomial regressions, where the dependent variable is a count of the number of times an individual has availed screening services. All the three models are re-estimated under the negative binomial model. As can be seen from Table 5, the signs and significance level of the independent regressors remained robust�. The primary variable of interest ‘Distance’ had the expected outcome on the dependent variable. As the Distance from the nearest cancer hospital increases, it significantly decreases the number of visits to screen for cancer by 15 to 23 percentage points. Similar to that of the probit models, Information received from the hospitals leaves the highest positive impact on screening. Individuals who had received information from hospitals regarding the importance of screening are 29 to 42 percentage points more likely to screen frequently. Finally, complementing the results of the probit models, individuals belonging to the highest income group and female patients are likely to screen more.

DISCUSSIONTo study the health care utilization services, we focused on

the cancer screening behavior of the patients. This is done to examine if uptake of preventative measures gets hindered by the barriers they face. The impact of distance and the role of information as predictors of health care utilization are estimated through probit and negative binomial regression models. Also, we did a spatial analysis to find clusters of cancer incidences in Kathmandu valley. Our analysis suggests that distance had a negative impact on screening uptake whereas any type of information from the hospital had a positive implication on the likelihood to screen and also on the frequency of screening. This implies that distance acts as a barrier, whereas availability of information acts as a facilitator to an individual’s decision to screen. Our results resonate the findings of some of the published literature on general health service utilizations [30-32].

The growing incidences of cancer in Nepal and the unavailability of screening facilities in most of the hospitals puts our present paper in perspective. On one hand, the super-specialty hospitals equipped with cancer technologies are limited in Nepal and on the other hand, distance has empirically proven to have a negative effect on the uptake rate. The unavailability of services coupled with distance as a barrier impede the dissemination of information. Lack of information and awareness further disrupts the uptake rate.

Other than Distance and Information, gender, employment status, and income of the household are also significant contributors to their decision to screen. We have found similar results in the literature where a higher income increases and

unemployment decreases the likelihood to screen [33-35]. Female, on the other hand are associated with higher probability of screening.

CONCLUSIONThe study had significant policy implications: First, most of

the cancer specialty hospitals are located in the Central region and do treat most cancer patients of the country. This is reflected in the spatial analysis. Moreover, when we measure the uptake of screening services as a function of distance, we found the negative implications that distance has on the likelihood of screening. This shows the need of a comprehensive cancer care network in the country. Second, we saw that a simple visual portray indicating the need for cancer screening tests can be instrumental in influencing the individual in taking up screening tests. We suggest that hospitals should be responsible for providing a minimum information regarding the availability and recommended ages of screening. The patients will value any information coming from the hospital as deemed necessary and thus the screening rate among the general population can significantly increase. The paper, however, has some following limitations and future research should be encouraged to that direction. First, the scope of the article limits us to provide any information on the causes and consequences of cancer. Thus, we are unable to reflect on any policies which can contribute to fewer incidences of cancer such as tobacco control policies given smoking is a risk factor for lung cancer. Instead, we recommend policies related to their preventative measures. Secondly, we only address the adult cancer and ignored the childhood cancer which can give us lower bound estimates to our barriers. Finally, we have limited our definition to barriers only to distance and information. Barriers can, however, take many forms including economic expenses and indirect cost borne on the primary caregiver by a cancer patient.

ACKNOWLEDGEMENTSWe express our deep sense of appreciation to ‘American

Cancer Society’ (ACS#21383) who provided us with the financial support to undertake the survey. We extend our sincere gratitude to members of Dhulikhel Hospital, Kathmandu University for providing us with all the technical support that was needed for the survey.

REFERENCES1. American Cancer Society. Cancer Facts & amp; Figures. Atlanta. 2016.

2. Binu V, Chandrasekhar T, Subba S, Jacob S, Kakria A, Gangadharan P, et al. Cancer Pattern in Western Nepal: A Hospital Based Retrospective Study Asian Pacific Journal of Cancer Prevention. 2007; 8: 183-186.

3. Indian Council of Medical Research. National Cancer Registry Programme India. 1981.

4. Dikshit R, Gupta PC, Ramasundarahettige C, Gajalakshmi V, Aleksandrowicz L, Badwe R, et al. Cancer mortality in India: a nationally representative survey. Lancet. 2012; 379: 1807-1816.

5. National Institute of Cancer Research and Hospital. Cancer Registry Report 2005-2007. Dhaka. 2009.

6. National Cancer Control Programme. Cancer Incidence in Sri Lanka. Colombo. 2006.

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Table 4: Marginal Effects of Probit regression.

VariablesModel 1 Model 2 Model 3All patients Female patients Cervical and Breast Regular Screening Regular Screening Regular Screening

Distance : 3-5 0.00520 0.0165 0.153(0.0292) (0.0555) (0.115)

Distance : 5-10 0.0920** 0.0456 0.0247(0.0449) (0.0453) (0.0446)

Distance: >10 -0.0755** -0.102*** -0.0471(0.0349) (0.0328) (0.0448)

Information 0.194*** 0.214*** 0.229***(0.0267) (0.0550) (0.0436)

Income: 20-30k 0.0331 0.223 0.0897(0.0566) (0.161) (0.319)

Income: >30k 0.0735** 0.206 0.361**(0.0340) (0.160) (0.148)

Education: Till Grade 8 -0.0352 -0.0559** -0.123***(0.0414) (0.0266) (0.0425)

Education: Grade 9-12 0.0362 -0.0152 -0.0824 (0.0594) (0.0651) (0.0839)Age 0.00212 0.00230* -0.00488***

(0.00169) (0.00132) (0.00161)Female 0.110* (0.0586)Unemployed -0.0453 -0.0754*** -0.112** (0.0347) (0.0252) (0.0471)Chherti -0.00357 0.0115 0.0599

(0.0615) (0.0709) (0.111)Newar 0.0172 0.000815 0.0221

(0.0384) (0.0610) (0.0263)Janajati 0.0549*** 0.0287 -0.00860

(0.0188) (0.0418) (0.0521)Madhesi_Dalit 0.0390 0.0917*** 0.0422

(0.0544) (0.0177) (0.0759)Observations 594 326 152Abbreviations: Distance: 3-5: It takes 3- 5 hours to reach the nearest cancer hospital; Distance: It takes 5-10 hours to reach the nearest cancer hospital; Distance: It takes >10 hours to reach the nearest cancer hospital; Income: 20-30k: Household income is between NRS 20 - 30,000; Income: >30k: Household income is greater than NRS 30,000; Education: Till Grade 8: The patient has studied till Grade 8; Education: Grade 9-12: The patient has studied more than Grade 9.

Table 5: Marginal Effects of Negative Binomial regression.Variables All Female Breast &CervicalDistance : 3-5 -0.05 0.05 1.10

(0.05) (0.11) (1.12)Distance : 5-10 0.12** 0.09* 0.06

(0.06) (0.06) (0.09)Distance: >10 -0.18*** -0.23*** -0.15* (0.06) (0.06) (0.08)Information 0.29*** 0.42*** 0.89

(0.06) (0.15) (0.58)Income: 20-30k 0.12 0.47 0.87

(0.10) (0.37) (2.59)Income: >30k 0.17* 0.34 1.47*** (0.10) (0.33) (0.55)Education: Till Grade 8 -0.04 -0.11 -0.52

(0.05) (0.09) (0.38)Education: Grade 9-12 0.10 0.03 -0.40

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(0.13) (0.23) (0.55)Age 0.00 0.01 -0.01

(0.00) (0.00) (0.01)Female 0.24* (0.13) Unemployed -0.03 -0.10*** -0.39 (0.03) (0.04) (0.25)Chherti -0.07 -0.15* -0.05

(0.06) (0.09) (0.25)Newar -0.02 -0.10 -0.23

(0.09) (0.16) (0.18)Janajati 0.09 -0.04 -0.04

(0.06) (0.06) (0.24)Madhesi_Dalit 0.10 0.13** 0.05 (0.08) (0.06) (0.17)N 594.00 326.00 152.00Abbreviations: Distance: 3-5: It takes 3- 5 hours to reach the nearest cancer hospital; Distance: It takes 5-10 hours to reach the nearest cancer hospital; Distance: It takes >10 hours to reach the nearest cancer hospital; Income: 20-30k: Household income is between NRS 20 - 30,000; Income: >30k: Household income is greater than NRS 30,000; Education: Till Grade 8: The patient has studied till Grade 8; Education: Grade 9-12: The patient has studied more than Grade 9.

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