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Ambulatory Diagnoses-Cluster statistics of patient visits at a clinic in the Amazon Region of Ecuador

Submitted: Tuesday, 21 August 2001

Revised: Friday, 23 November 2001

Published: Sunday, 2 December 2001

Samaan, R., Nemes, A., Pearce, K., Matheny, S., Crockett, S., Seydel, K.

Samaan, R., Nemes, A., Pearce, K., Matheny, S., Crockett, S., Seydel, K.,  (2001), Ambulatory Diagnoses-Cluster statistics of patient visits at a clinic in the Amazon Region of Ecuador, Rural and Remote Health 1,


Mondana Clinic is a small rural clinic located in the Napo river region of the Amazon basin in Ecuador. Since it’s opening in 1997 it has grown to be the primary health care facility for approximately 3,000 individuals.

A retrospective study was performed tabulating the ambulatory diagnosis, age, sex, and domicile of patients over a 9 month period of 1999. Over this period there were 765 patient visits that resulted in at least one diagnosis. 175 (22.8%) of the patient visits resulted in multiple diagnoses. Females accounted for 58% of the patient visits, remarkably similar to the 60% of ambulatory patient visits made in the United States by women. The age distribution showed 66% of patients being under 25 years of age.

When comparing diagnoses of males and females several differences were noted. As expected, urinary tract infections were approximately four-fold more common in females than in males. Gastritis and headaches were also more common reason for patient visits in the female population than in the male. Conversely lacerations, abrasions, and contusions ranked higher in the male population than in the female for patient visits.

This study is the first to provide public health information for this region that will prove useful to the health professionals and funding agencies working in the region. Furthermore, it provides a baseline for comparison to other regions in Ecuador and South America in general, as well as comparisons with data-rich countries such as the United States.


The purpose of this study was to retrospectively collect health statistics data from the Mondaña Health Clinic in Mondaña, Ecuador. The clinic is a project of FUNEDESIN (Foundation For Integrated Education and Development)-a non profit non governmental organization (NGO) based in Quito, Ecuador. The Mondaña clinic receives support from MAP International (MAP International is a nonprofit Christian relief and development organization) via USAID funding.

The Mondaña Health Clinic

In 1992, FUNEDESIN began community development and educational outreach in the Napo Province along the Napo River. Its field headquarters is located in the community of Mondaña, located two hours (by motorized canoe) from the nearest main port of Misahuallí. There are no access roads, electricity, or telephones in the region. The Mondaña Clinic opened in October of 1997 and is serving over 3,000 people in communities along the Napo River. The population base of the Mondaña Clinic service area is primarily indigenous Quichuas, along with some colonists who have migrated from the highlands and coastal regions of the country.

The project has been funded by private, corporate, and non-profit foundation donations1.

Although accurate data has been tabulated since the inception of the clinic three years ago, there have been no attempts at collecting, organizing, and analyzing this data. Data generated from such analysis could prove useful to the physicians as well as the funding organizations. This health data provides a baseline measure of the major health diagnoses in the clinic, which will facilitate comparison to other regions of the Amazon. Furthermore, our data analysis of patient visits provides preliminary estimates of the epidemiology of the major diseases of patients seen at the clinic. Schneeweiss et al., Williams et al., and Marsland et al., have demonstrated the usefulness of such data in describing the specialty of family practice and other primary care professions2,3,4. We hope to do the same in the Amazon region. We also undertook a brief comparison between this data and comparable data from the US to demonstrate the considerable differences in activity in these two health systems.


An ACCESS database was designed to enter data collected from daily patient reports, which were transcribed by the physicians while documenting more detailed information in patient’s chart. The variables collected and entered in the database include the following: 1) date of consultation, 2) age, 3) sex, 4) community, 5) patient’s name and health record number, 6) diagnoses, and 7) diagnosis cluster. The local physicians helped clarify and categorize certain diagnoses.

Data was collected from daily health reports between January and September 1999 (N=765 patients) rather than until the end of the year because our time in Mondaña was limited. To ensure the validity and reliability of the data, the clinic number and names were filtered and matched to insure consistent data in terms of age and home community of the patients. Any inconsistencies were followed up by looking directly at patients health records.

The diagnostic clusters were adapted from Schneeweiss et al.,3 and Williams et al.4, however, slight adjustments were made to accommodate for the international context. For example, because of the high prevalence of parasitic infections (rather than infectious diseases), a specific category recognizing parasitic infections as a sole independent category was created. We also used diagnostic clusters to help facilitate a general overview, without sacrificing details of the diagnoses clusters. As Schneeweiss writes: “…Diagnostic clusters capture the great majority of ambulatory patient visits and result in a more manageable number of clinically related entities”5.

Data was analyzed by creating queries in ACCESS and then creating specific categories to filter the data (i.e. sex, disease category, and domicile). Due to a shortage of time, we were unable to collect data regarding home community for all patients. EPI Info 2000 was used to analyze the data by generating frequencies and means. Excel 97 was used for further calculations of percentages and creating pie charts and histograms. Fisher Exact Test to compare data between males and females were performed using Analyze-It Software.


The total number of patient visits collected from the months of January 1999 through September 1999 totaled 765 patient visits with at least one diagnosis. 175 patient visits had 2 diagnoses. In Table 1 and Figure 1, the demographic distribution found that of the 765 patient visits( note: it is possible to be a repeat patient), 319 (319/753=42%) patient visits were male and 434 (434/753=58%) were female. 66% of the patient visits were under the age of 25 years old. In most age groups, females outnumbered males for patient visits, but this was not statically significant. However, there were more male patients in the 46-50 and greater than 51 year old age group. Diagnoses were always made on the basis of the patient’s history and physical exam. In some cases, rudimentary laboratory tests (e.g. urinalysis, stool ova and parasites, and hematocrit) were used to supplement the clinicians' diagnostic capabilities.

Figure 1

Table 2 shows the major differences and similarities in diagnoses between the sexes for patient visits. For both males and females, the top two diagnoses clusters were acute upper respiratory tract infection and parasitic infection. However, despite these similarities, males patient visits had a higher frequencies of URI (24.7%) and parasite infection (19.7%) than female patient visits (17.3% for both).

In Figure 3, female patient visits had pregnancy care ranked third with 14.8%, urinary tract infection fourth with 13.6%, and lacerations, abrasions, and contusions fifth with 5.4%.

Figure 3

In Figure 4, male patient visits had lacerations, abrasions, contusions ranked higher than females at third with 9.7%, fungal infection ranked forth at 8.4%, and dermatitis ranked fifth at 6.0%. Gastritis also ranked higher in females than in males.

Figure 4

Using the Fischer Exact test, three diagnoses-clusters were found to be statistically significant. Male patient visits had 24.7% of visits due to acute upper respiratory tract infections compared to 17.3% for females (p=0.0457). Also, female patient visits had a fourfold higher frequency of urinary tract infections compared to male patient visits (13.6% vs 3.0% p<0.001). Finally, male patient visits had a twofold higher frequency of being diagnosed with fungal infections compared to female patient visits (8.4% vs 3.3% p=0.008).

In table 3, there are minimal differences between patient visits for male and female children in regards to the top five diagnostic clusters. Again acute URIs and parasite infections were the major diagnoses at the health clinic, but in this case, male patient visits had higher rates than female patient visits in the acute URI category 42.5% vs 36.4%) and female patient visits had higher rates in the parasite infection category (29.9% vs 21.9%). Female patient visits had higher rates of diarrhea (diarrhea not otherwise specified—most parasitic infections cause diarrhea) than male patient visits (13.0% vs 8.2%). But male patient visits (15.1%) had higher rates of fungal infection than female patient visits (not in top 5, but at 3.7%). Male and female patient visits had dermatitis in the fourth position, but male patient visits had higher rates than female patient visits (12.3% vs 11.7%).

Table 4 shows the patient’s home community. Mondaña has the highest frequency of patient visits to the clinic because the clinic is located in Mondaña. However, the distance from the clinic does not predict patient visit rates. For example, Yuralpa Izquierda is farther away from Mondaña than 30 de Agosto, but still had more patient visits to the clinic.


There were some significant differences in patient visits to the Mondaña Health Clinic between males and females. For example, 58% of the patients visiting the Mondaña Health Clinic were females compared to 42% males. When compared to the most recent US data on ambulatory visits, women represented 60.3% of all patient visits to primary care physicians compared to 39.7% male patient visits6.

There are also some major differences between the age distribution in the Mondaña health clinic, compared to the US population ambulatory visits. For example, 66% of the patients visiting the Mondaña Clinic were 25 years old or younger. In the US data, 17.6 % of the patients were less than 15 years old and 8.6% of the population were15-24 years old. If the latter two categories are combined, this equals 26.2% of the population, which is much less than the 66% that are 25 years old or younger. One indication is the large difference in the ages of the populations is due to the lower life expectancy in the Amazon region. For example, although there are no statistics for the life expectancy of people living in the Amazon region, in Quito life expectancy is 68.8 years (66.4 for males and 71.4 for females)7.

There were major differences in diagnoses between males and females visiting the Mondaña Health Clinic. Many of these differences in patient visits between males and females is based on the fact that women were using the clinic for different reasons, which include prenatal care and deliveries and males were using the clinic only in acute emergency situations. For example, males were visiting the clinic for acute injuries more than females, which is consistent with the type of physical labor that men do in that area (i.e. agriculture). However, women were using the clinic to deliver their children and to seek prenatal and postnatal care. Interestingly, normal pregnancy was the second most common diagnoses for females in the US and in the Mondaña Clinic, it ranked third behind URIs and parasite infections.

Women also had more patient visits for gastritis compared to men (4.9% vs 2.7%). The higher rates of gastritis in women may be due to higher rates of Chicha (alcoholic beverage of the native people) consumption because women are traditionally the producers of Chicha, which originates from fermented yucca root/manioc. In regards to the US data, surprisingly the most common diagnosis cluster was acute upper respiratory tract infections at 4.1 % of all patient visits (females 3.7% and males 4.8%)6. Similar to the Mondaña Health Clinic, patient visits for males and females ranked highest for acute upper respiratory tract infections, and similar to the US data, males had higher rates than females for patient visits for URIs (24.7% versus 17.3% p=0.0457), which is statistically significant.

Another major significant finding was the fourfold difference between female and male paient visits for cases of urinary tract infections (females 13.6% vs 3.0% p<0.001). Although UTIs, did not rank in the top 15 most common diagnoses in the US data, there is evidence to suggest that females have a 50% higher risk of UTIs than males in the US8. The higher rates may be due to a combination of poor hygiene aggravated by a more humid climate. Also the different rates in males and female can largely be attributed to the anatomical juxtaposition of the GU and GI tract termini in the human female. Finally, male patient visits had a two-fold higher frequency of being diagnosed with fungal infections compared to female patient visits (8.4% vs 3.3% p=0.008). The major diagnoses in the US (see Table 5) were not ranked in the top 15 major diagnoses in Mondaña (i.e. essential hypertension, diabetes mellitus, malignant neoplasm, ischemic heart disease).

This data gathered will be of importance for those interested in working in the Mondaña Health Clinic. Visiting physicians will be able to prioritize resources and funding agencies will be able to use this information to compare to other sites in the region as well as a baseline against future preventive public health interventions.

Some limitations to the data are the validity of the diagnosis. For example, although gastritis is a pathologic diagnosis based on lab tests and endoscopy, this diagnosis was made entirely on the basis of the patient’s history and physical exam. Also, parasitic and fungal infection diagnoses are made without confirmatory laboratory procedures. Furthermore, the lack of separation of the children and adults into separate groups may skew the data and lead to selection bias because children are more prone to develop certain diseases. Moreover, although the comparison between patients in Mondaña and the US demonstrated differences in health systems, the fact that the social, political, economic, and geographic differences are extensive deemed this comparison invalid. However, one of the reasons for the comparison was that the US statistics are easily accessible.

Future studies should compare Mondaña to other South American or other developing countries. Also, the fact that our study period was nine months rather than twelve months limits the validity of our study because this does not take into account seasonal variation over twelve months. The reasons for collecting data for nine months was based on time limitations.


Rodney Samaan was the principle investigator for this study. Dr. Karl Seydel was involved in the preparation of the manuscript as well as the design of the graphs and calculation of the statistics. Seth Crockett and Alison Nemes were involved in the review of the manuscript. Dr. Matheny and Dr. Pearce provided guidance for this study and manuscript.

The authors would like to thank Douglas McMeekin for his hard work and perseverance in creating FUNEDESIN. Our thanks also goes out to Claire Boswell and Marci Stoterau from MAP International, whose support was constant. The staff of the Mondana Clinic was invaluable and incredible. We also want to thank Dr. Peli Grosa from the University of Kentucky for help in editing this manuscript . Finally, our gratitude goes out to University of KY Basic Research Foundation who funded the trip to Ecuador.


(1) FUNDESIN (Foundation for Integrated Education and Development) Nov 23, 2001:

(2) Marsland DW. Wood M. Mayo F. 'Content of Family Practice. Part I. Rank order of diagnoses by frequency. Part II. Diagnoses by disease category and age/sex distribution. J Fam Pract 1976; Feb;3(1): 37-68.

(3) Schneeweiss R. Cherkin D. Hart G. et al. Diagnosis Clusters Adpated for ICD-9-CM and ICHPPC-2. Journal Family Practice 1986; 22(1): 69-72.

(4) Williams B. Philbrick J. Becker D. McDermott A. Davis R. Buncher P. A Patient-based System for describing Ambulatory Medicine Practices Using Diagnosis Clusters. Ambulatory Medicine 1991; 6: 57-63.

(5) Schneeweiss R. Hart G. Diagnostic Content of Ambulatory Primary Care: Implications for Resource Utilization. J. Ambulatory Care 1988; 11(3): 13-22.

(6) Woodwell D. National Ambulatory Medical Care Survey: '1998 Summary. Advance Data, Number 315, July 19, 2000, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.'

(7) Ecuador: Health in the Americas, 1998, Volume II. 240-257,

(8) Isselbacher K. et al. Harrison’s Principles of Internal Medicine, 13th edn. McGraw Hill Inc, 1994.

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